U.S. patent number 8,793,171 [Application Number 13/009,156] was granted by the patent office on 2014-07-29 for electronic system for analyzing the risk of an enterprise.
This patent grant is currently assigned to EquityNet, LLC. The grantee listed for this patent is Judd E. Hollas. Invention is credited to Judd E. Hollas.
United States Patent |
8,793,171 |
Hollas |
July 29, 2014 |
Electronic system for analyzing the risk of an enterprise
Abstract
An automated and interactive system that facilitates efficient
capitalization/liquidation and monitoring of private and
publicly-traded enterprises through a network-driven marketplace is
disclosed. The system may be comprised of a dynamic process for
enterprise characterization, a customizable computational engine
that utilizes statistical reference information to quantify a
multi-factor scoring value for each unique enterprise, a
customizable system for investor-users to filter, rank, and screen
enterprise prospects, a customizable system for monitoring the
performance of enterprises, an integrated internal system for
electronic communication between market participants, and an
empirical feedback system that provides a dynamic knowledge base of
statistical reference information for various computational
components of the invention.
Inventors: |
Hollas; Judd E. (Fayetteville,
AR) |
Applicant: |
Name |
City |
State |
Country |
Type |
Hollas; Judd E. |
Fayetteville |
AR |
US |
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Assignee: |
EquityNet, LLC (Fayetteville,
AR)
|
Family
ID: |
37997711 |
Appl.
No.: |
13/009,156 |
Filed: |
January 19, 2011 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20110161245 A1 |
Jun 30, 2011 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
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11904573 |
Sep 7, 2007 |
7908194 |
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11266572 |
Apr 13, 2010 |
7698188 |
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Current U.S.
Class: |
705/35 |
Current CPC
Class: |
G06Q
40/00 (20130101); G06Q 40/06 (20130101); G06Q
10/0639 (20130101); G06Q 10/06375 (20130101); G06Q
10/0635 (20130101); G06Q 10/10 (20130101) |
Current International
Class: |
G06Q
40/00 (20120101) |
Field of
Search: |
;705/35-40 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Akintola; Olabode
Attorney, Agent or Firm: Dougherty; J. Charles
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This is a divisional application of prior application Ser. No.
11/904,573, filed on Sep. 27, 2007, entitled "Electronic Enterprise
Capital Marketplace and Monitoring Apparatus and Method," which is
in turn a continuation-in-part of prior application Ser. No.
11/266,572, now U.S. Pat. No. 7,698,188, filed on Nov. 3, 2005, and
entitled "Electronic Enterprise Capital Marketplace and Monitoring
Apparatus and Method." Such applications are each incorporated
herein by reference.
Claims
The invention claimed is:
1. A computer system for analyzing the risk of a private
enterprise, comprising: (a) at least one of (i) an enterprise
characterization module resident on a server system comprising at
least one server and configured to receive from an enterprise-user
terminal information concerning an enterprise characterization, or
(ii) an archival database resident on said server system and
configured to receive information from external sources, wherein
said server system and said enterprise-user terminal are connected
through a computer network; (b) a knowledge base module resident on
said server system and configured to store and access statistical
reference correlation information; and (c) a risk model module
resident on said server system and configured to receive
information from said at least one of the (i) enterprise
characterization module or (ii) archival database and said
knowledge base module, and generate an output comprising a private
enterprise risk scoring value associated with the private
enterprise.
2. The system of claim 1, comprising an enterprise characterization
module and wherein said enterprise characterization module is
configured to request certain information concerning the enterprise
that is determined by and based originally upon a characterizing
categorization of the enterprise.
3. The system of claim 1, comprising an enterprise characterization
module and wherein said enterprise characterization module is
configured to request information concerning the enterprise
dynamically, whereby a subsequent query is based upon a response to
a previous query.
4. The system of claim 1, comprising an enterprise characterization
module wherein said enterprise characterization module is further
configured to store incomplete information for fulfillment of
information at a later time.
5. The system of claim 1, comprising an enterprise characterization
module wherein said enterprise characterization module is further
configured to generate output comprising feedback concerning at
least one of the quality and adequacy of data input to said
enterprise characterization module.
6. The system of claim 1, wherein said risk model module is
operable to receive information concerning at least one of a set of
desired weighting parameters to be used in calculations by said
risk model module.
7. The system of claim 1, further comprising an archival database
module resident on said server system and configured to store and
access at least one of empirical and longitudinal information
comprising at least one of original enterprise-related
characteristics and post-funding enterprise-related performance
characteristics.
8. The system of claim 1, further comprising a risk correlation
module resident on said server system and configured to identify in
an archival database statistical correlations to be stored in said
knowledge base module and used by said risk model module.
9. The system of claim 8, wherein said risk correlation module is
configured to compute reference risk correlations between
enterprise-related attributes and enterprise risk for
characteristically similar cross-sections of an enterprise
domain.
10. The system of claim 9, wherein said reference risk correlations
are empirically comprised of at least one of dichotomous enterprise
success and failure and a degree of deviation of actual enterprise
performance from a projected performance.
11. The system of claim 1, wherein said risk model module is
configured to identify based on the characteristic classification
of an enterprise a set of relevant reference risk correlations
within said knowledge base module.
12. The system of claim 1, wherein said risk model module is
configured to compute, using relevant reference risk correlations
from said knowledge base module, at least one of a mean value of
risk and associated probability distribution of risk that is
associated with each risk-correlated enterprise-related
attribute.
13. The system of claim 12, wherein said risk model module is
configured to aggregate at least one of said mean values of risk
and associated probability distributions of risk into a risk value
and risk distribution, respectively.
14. The system of claim 12, wherein said risk model module is
configured to aggregate said mean values of risk and associated
probability distributions of risk according to a certain weighting
factor for each risk-correlated enterprise attribute, the weighting
factor of which is a function of the statistical significance of
each enterprise attribute related reference risk correlation.
15. The system of claim 1, wherein said risk model module is
further configured to compute an aggregation of systematic and
unsystematic risk inherent to the enterprise.
16. The system of claim 1, wherein said risk model module is
further configured to compute at least one of a probability of
failure and probability of success for the enterprise.
17. A computer system for analyzing a private enterprise,
comprising: (a) at least one of (i) an enterprise characterization
module resident on a server system comprising at least one server
and configured to receive from an enterprise-user terminal
information concerning an enterprise characterization, or (ii) an
archival database resident on said server system and configured to
receive information from external sources, wherein said server
system and said enterprise-user terminal are connected through a
computer network; (b) an enterprise analyzer module resident on
said server system configured to receive at least one of (i)
information from said enterprise characterization module, or (ii)
information from said archival database, and further configured to
generate a multi-factor private enterprise scoring value associated
with the private enterprise.
18. The system of claim 17, further comprising an investor
requirements module resident on said server system and configured
to receive from an investor-user terminal information concerning
investor requirements for use by at least one of said enterprise
characterization module, said enterprise analyzer module, or a risk
model module, wherein said server system and said investor-user
terminal are connected through a computer network.
19. The system of claim 18, wherein said investor requirements
module is configured to receive information concerning one of a set
of desired adjustments to enterprise data obtained from said
enterprise characterization module to be used by at least one of
said enterprise analyzer module and said risk model module.
20. The system of claim 17, wherein said archival database module
is further configured to access and store information comprising at
least one of original enterprise-related characteristics,
information from third-party external sources, and future
enterprise-related performance characteristics.
21. The system of claim 17, further comprising a knowledge base
module resident on said server system configured to access and
store statistical reference information to be used by said
enterprise analyzer module and a risk model module.
22. The system of claim 17, further comprising a correlation
development and feedback module resident on said server system
configured to identify, in said archival database, statistical
correlations to be stored in a knowledge base module and used by
said enterprise analyzer module and a risk model module.
23. The system of claim 17, wherein said enterprise analyzer module
further comprises a user input validation module configured to
validate and invalidate information from said enterprise
characterization module.
24. The system of claim 17, wherein said enterprise analyzer module
comprises a user input reconciliation module configured to
reconcile and adjust invalid information from said enterprise
characterization module.
25. The system of claim 17, wherein said enterprise analyzer module
is configured to compute a fair market valuation of the
enterprise.
26. The system of claim 17, wherein said enterprise analyzer module
is configured to generate a market comparison of a specific
enterprise of interest to relevant peer enterprises in terms of at
least one of a risk value, risk-adjusted internal rate of return,
risk-unadjusted internal rate of return, valuation, or information
concerning an enterprise characterization for a plurality of
enterprises.
27. The system of claim 17, further comprising a correlation
development and feedback module resident on said server system
configured to aggregate and organize, in an archival database, said
information concerning an enterprise characterization for a
plurality of enterprises to be stored in a knowledge base.
28. The system of claim 17, wherein said enterprise analyzer module
is further configured to identify, based on a characteristic
classification of said enterprise, aggregated and organized
information concerning an enterprise characterization for a
plurality of enterprises within a knowledge base module.
29. The system of claim 17, wherein at least one of said
enterprise-user terminal and an investor-user terminal is further
configured to receive from said enterprise analyzer module and
display aggregated and organized information concerning an
enterprise characterization for a plurality of enterprises.
30. The system of claim 17, wherein said enterprise analyzer module
is configured to transmit to and display at least one of said
enterprise-user terminal and an investor-user terminal at least one
of said information from said external sources and said information
concerning an enterprise characterization.
31. The system of claim 17, wherein said multi-factor private
enterprise scoring value comprises at least one of a private
enterprise risk scoring value, a performance scoring value, a
competitive scoring value, or other scoring values with utility in
ranking private enterprises.
Description
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
Not applicable.
BACKGROUND OF THE INVENTION
The present invention relates to a method and system for the
formation of an electronic network-based capital marketplace that
facilitates efficient capitalization and liquidation of enterprises
by market participants through utilization of enterprise
search-and-sort and associated decision support systems. The
present invention also relates to an integrated method and system
for efficient electronic monitoring of enterprise performance.
Through its enabling role in the capitalization of new and emerging
enterprises, the market for private equity and debt capital
constitutes an essential pillar of modern capitalism. A lack of
integrated process automation and considerable market
fragmentation, however, constrain investors' ability to
collectively create an efficient market for private capital. A
leading study from Harvard University found that "efficient markets
do not exist for allocating risk capital to early-stage technology
ventures and that serious inadequacies exist in information
available to both entrepreneurs and investors." The prevalence of
such inefficiencies in a significant capital market like private
equity imposes limitations on investors and entrepreneurs alike,
but most importantly, these inefficiencies fundamentally limit the
efficient, free-market premise of modern capitalism.
Current investor "deal-flow" (i.e., enterprise identification and
screening) practices rely largely on fragmented networks of
non-stakeholders for prospect identification, and subsequently on
manually intensive screening processes for initial qualification of
these enterprise prospects (in lieu of the due diligence process).
Considerable inherent market fragmentation inhibits efficient
matching of enterprise agent and investor agent groups, and manual
screening processes employed by investor agents limit their
potential rate of enterprise exposure. In addition, these referral
networks restrict the velocity of information flow, and hence
inhibit the ultimate rate at which capitalization and liquidation
decisions are made. For entrepreneurs, poor availability and high
costs of capital associated with current practices can restrict
their ability to survive and grow. The substantial time and
attention demands of current practices distract entrepreneurs from
their critical operational responsibilities. For other enterprise
agents seeking an enterprise liquidity event, conventional market
practices are, in aggregate, ineffective at producing adequate
marketplace liquidity.
Once capitalized, the performance of young enterprises is typically
monitored by investors to minimize the probability of failure and
maximize the investors' return on capital. However, one-third of
young enterprises typical fail within three years of
capitalization, indicating that investors have in general not
implemented an effective systematic method for adequately
monitoring the performance of their portfolio enterprises. Studies
have determined that around 50% of business failures could have
been avoided if related indications of incipient failure had been
detected early enough, thereby identifying the need for a
systematic method of enterprise performance monitoring and emerging
failure detection.
Since the Internet presents an effective communication platform for
the sharing of information such as enterprise business plans with
potential investor agents, several online entities have established
rudimentary network-based platforms for enterprise agents to submit
and share their business plans with member investor agents. None of
these intermediates, however, have systematically employed process
automation that advances and improves the process beyond
conventional practices. The only distinguishing feature of these
processes beyond conventional investor deal-flow practices is that
they have utilized the Internet as a central location for
communication between both parties. Since they have failed to
introduce procedures and technologies that engender a more
efficient process, the industry has been incapable of facilitating
an efficient marketplace for private capital.
The risk (i.e., probabilistic uncertainty) associated with the
expected fiscal performance of an enterprise asset is comprised of
both systematic (economy-based and market-based) risk and
unsystematic (firm-based and industry-based) risk. These risk
categories are functions of various endogenous (e.g., cash flow
management) and exogenous (e.g., interest rates) factors inherent
to the enterprise. Enterprises in specific industry sectors exhibit
sufficiently similar risk profiles such that specific risk factors
are largely consistent in these near-homogenous cross-sections of
the enterprise domain. Empirically, studies have determined that
certain identifiable enterprise attributes of endogenous and
exogenous form exhibit a statistically significant correlation with
enterprise risk and can be used as a knowledge reference to compute
and predict the risk inherent to a specific enterprise.
Over the years, academic researchers have developed numerous
techniques for enterprise failure prediction, including: classical
cross-section statistical methods, machine learning decisions
trees, neural networks, fuzzy rules-based classification model,
multi-logic model, cumulative sum model, dynamic event history
analysis, catastrophe theory and chaos theory model,
multidimensional scaling, linear goal programming, multi-criteria
decision aid approach, rough set analysis, expert systems, and
self-organizing maps. Of all these methods, the majority of peer
review studies find that conventional multivariate statistical
techniques and neural network techniques generally perform best.
However, several investigations have found that the performance of
neural network techniques is subject to "over-fitting" that may
result in an overstated accuracy for the neural network in
comparison to the other techniques.
Some techniques for valuing an enterprise have been described in a
number of patent applications, including the disclosures of U.S.
Pat. Application Publication Nos. 2002/0174081 to Charbonneau et
al. and 2004/0024674 and 2004/0128174 to Feldman. While these
techniques are asserted to be applicable to private enterprises,
they are devoid of any technique for validation and reconciliation
of the input consisting of enterprise attributes, which often can
be erroneous due to subjective and biased sources of origination
(i.e., entrepreneurs seeking capital). It is well accepted within
the relevant arts that the current value of an asset is a function
of the asset's expected generation of future free cash flows, each
of which is discounted at a rate of risk (i.e., cost of capital).
Neither valuation technique is capable of augmenting projected
perpetual free cash flows by the statistically computed unique
endogenous and exogenous risk profile of an enterprise to compute
the risk-adjusted valuation of an enterprise. Specifically, the
disclosure of U.S. Patent Application Publication No. 2002/0174081
requires comparable metrics of current enterprises in order to
train its neural network and determine a current enterprise
valuation, a method which is highly sensitive to market deviations
from efficient asset pricing as experienced in the excessive
speculation in the late 1990s.
Some techniques for quantifying the risk of an enterprise have been
described in a number of patent applications, including the
disclosures of U.S. Patent Application Publication Nos.
2004/0044617 to Lu, 2004/0044505 to Horwitz, and 2002/0147676 to
Karmali. In general, these techniques restrict their consideration
of enterprise risk to a finite group a factors that constitute
symptomatic indications of enterprise risk. Their inadequacy
results from an inability to incorporate a dynamic collection of
endogenous and exogenous parameters that represent root causes of
enterprise risk. Specifically, U.S. Patent Application Publication
Nos. 2004/0044617 and 2002/0147676 do not fully automate or
disclose their process of risk quantification and require the user
to input subjective parameters that serve as reference values in
the quantification of risk. Their primary relative inadequacy lies
in their lack of a systematic method for dynamically incorporating
new and evolving statistical reference information that correlates
endogenous and exogenous enterprise-related attributes with
dependent parameters representing enterprise risk.
Some techniques for matching entrepreneurs and investors have been
described in a number of patents applications, including U.S.
Patent Application Publication Nos. 2002/0138385 to Milam and
2002/0087450, 2002/0087446, 2003/0101115, and 2002/0087506 to
Reddy. A majority of the investors to which these techniques are
targeted generally employ complex and intuitive rule-based methods
in their screening and ranking of enterprise investment prospects.
While the techniques embodied in the referenced prior art allow for
rudimentary criteria-based matching of investors and entrepreneurs,
they do not provide the systematic functionality necessary to
conform automated methods to existing practices in such a way that
engenders an efficient process, and hence do not provide an
efficient market for private enterprise capitalization. For
example, none of the prior art enables investors with high degrees
of freedom in enterprise search criterion or the capability to rank
enterprise matches through a system that is capable of
incorporating specific investor preferences in a computation of a
multi-factor enterprise scoring value.
Individually, techniques have been described for enterprise
valuation, enterprise risk assessment, and Internet-based
enterprise agent and investor agent matching. No prior art
techniques have been described that provide an integrated system
for aggregating enterprise risk and valuation analysis, enterprise
agent and investor agent matching, and enterprise monitoring in a
construct that is capable of creating an efficient marketplace.
Such a system and method would be highly desirable by market
participants and effective at improving productivity and liquidity
within an industry that controls close to $1 trillion in capital
and that is responsible for the original funding of one third of
U.S. public companies.
SUMMARY OF THE INVENTION
It is an objective of the present invention to overcome drawbacks
of the prior art by providing a method and system that facilitates
efficient capitalization/liquidation and monitoring of private and
publicly-traded enterprises. In various embodiments, the system is
comprised of (i) a dynamic process for enterprise characterization,
(ii) a highly customizable system for investor agents to filter,
rank, and screen enterprise prospects, (iii) a computational engine
that utilizes statistical reference correlations to quantify a
multi-factor enterprise scoring value for each unique enterprise,
(iv) a system for automated or interactive monitoring of the
performance of enterprises, (v) an integrated internal system for
electronic communication between market participants, (vi) and a
dynamic empirical feedback system that provides a knowledge base of
statistical reference information for various computational
components of the invention.
According to the various embodiments of the present invention,
enterprise agents have the capability to submit information that
characterizes their enterprise and investor agents have the
capability to utilize customizable search-and-sort technology to
screen large volumes of enterprises and efficiently identify a
finite number of enterprises for further due diligence and
potential investment. The core enterprise analysis engine of this
invention is capable of automatically quantifying a multi-factor
enterprise scoring value for enterprises. The software of the
present invention is coupled with robust database search
capabilities to produce an e-marketplace solution that allows
investor agents to efficiently screen and rank potentially
thousands of enterprises based on specific user-defined deal-flow
preferences (e.g., enterprise type, CEO education, IP status, etc).
This unique combination of automated techniques facilitates the
creation of an efficient marketplace for intelligently matching
enterprise agents who seek capital or a liquidity event with
investor agents who seek investment opportunities.
The invention overcomes limitations of the prior art in various
embodiments by providing a unique integration of novel automated
systems that collectively provide the functionality necessary for
creation of an efficient marketplace for enterprise
capitalization/liquidation and monitoring. The invention provides a
secure, independent, and accessible platform that utilizes
search-and-sort technology to efficiently and intelligently match
enterprise agents and investor agents. It dramatically reduces
recognized industry deficiencies by combining automated decision
support systems and a comprehensive suite of services in the form
of an e-marketplace that offers a single destination for enterprise
agents to find capital or a liquidity event and for investor agents
to screen enterprise prospects, obtain independent due diligence,
and monitor the progress of enterprises.
The invention enables investor agents to efficiently track and
benchmark the ongoing performance of multiple enterprises in
various embodiments via the use of an automated and interactive
enterprise monitoring system. Enterprise agents can conveniently
access the web-based monitoring system to periodically report
enterprise performance for review by relevant investor agent(s).
Investor agents can set benchmarks and thresholds that generate
automatic investor agent notification if intersected, statistically
predict future enterprise performance and probability of failure,
or use robust interactive analysis tools to intelligently monitor
enterprise progress.
These and other features, objects, and advantages of the present
invention will become better understood from a consideration of the
following detailed description of the preferred and alternative
embodiments and appended claims in conjunction with the drawings as
described following:
DRAWINGS
FIG. 1 is a schematic diagram of hardware components used in a
preferred embodiment of the present invention.
FIG. 2 is a diagram depicting logical elements of a preferred
embodiment of the present invention.
FIG. 3 is a diagram depicting logical elements of an assess and
score and search and sort subsystem according to a preferred
embodiment of the present invention.
FIG. 4 is a diagram depicting logical elements of an enterprise
analysis subsystem according to a preferred embodiment of the
present invention.
FIG. 5 is a diagram depicting logical elements of a risk model
subsystem according to a preferred embodiment of the present
invention.
FIG. 6 is a diagram depicting exemplary risk-associated parametric
correlations according to a preferred embodiment of the present
invention.
FIG. 7 is a diagram depicting an example of the results of
customization of reference correlations according to a preferred
embodiment of the present invention.
FIG. 8 is a diagram depicting an example of the computation of risk
distribution and risk value according to a preferred embodiment of
the present invention.
FIG. 9 is a diagram depicting an exemplary plot of heuristic
feedback of knowledge-base customizations according to a preferred
embodiment of the present invention.
FIG. 10 is a diagram depicting logical elements of an enterprise
monitoring system according to a preferred embodiment of the
present invention.
FIG. 11 is a diagram depicting logical elements of the automatic
enterprise monitor component of the enterprise monitoring system
according to a preferred embodiment of the present invention.
FIG. 12 is a diagram depicting the interactive enterprise monitor
component of the enterprise monitoring system according to a
preferred embodiment of the present invention.
FIG. 13 is an illustration of an exemplary user screen displayed by
the interactive enterprise monitor system when an inventory
turnover ratio parameter is selected according to a preferred
embodiment of the present invention.
FIG. 14 is an illustration of an exemplary user screen displayed by
the interactive enterprise monitor system when a revenue parameter
is selected according to a preferred embodiment of the present
invention.
FIG. 15 is a diagram depicting logical elements of an enterprise
analysis system according to an alternative embodiment of the
present invention.
FIG. 16 is a diagram further depicting logical elements of an
enterprise analysis system according to an alternative embodiment
of the present invention.
FIG. 17 is a diagram depicting logical elements of an enterprise
risk model system according to an alternative embodiment of the
present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
With reference to FIG. 1, the hardware and network components used
in the implementation of a preferred embodiment of the present
invention may now be described. The present invention is intended
to be used by enterprise agents who are seeking investment monies
or a liquidity event, as well as investor agents who are seeking to
find enterprises in which to invest. For purposes herein, an
"enterprise-user" will be any stakeholder, representative, or agent
who interacts with the system on his/her own behalf or on behalf of
the stakeholders of a particular enterprise or enterprises.
Likewise, an "investor-user" will be any investor, representative,
or agent who interacts with the system on his/her own behalf or on
behalf of a particular investor or investors or potential investor
or investors. In the preferred embodiment, each enterprise-user and
investor-user is assumed to access the system from an
enterprise-user terminal 2 and investor-user terminal 4,
respectively. Although only one each of these terminals are shown
in FIG. 1, the preferred embodiment would allow a number of
enterprise-users and investor-users to access the system by means
of different terminals 2 and 4, respectively, maintained by each
such party. Terminals 2 and 4 are preferably personal computers,
but may also be any other device capable of sending and receiving
textual and graphical information over a network. Both terminals 2
and 4 are linked to network 6, which in the preferred embodiment is
the Internet.
The various functionality of the preferred embodiment is
implemented primarily by means of software that is run from server
8. Server 8 is connected by means of network 6 to each
enterprise-user terminal 2 and investor-user terminal 4. In the
preferred embodiment, the application software running at server 8
is provided by an independent party as an application service
provider (ASP). Using this model, all proprietary software resides
at server 8, and the only software required to use the system at
enterprise-user terminals 2 and investor-user terminals 4 is the
software needed to access network 6, which for the Internet may be
an Internet browser. In an alternative embodiment, the software may
include a proprietary access component that must be installed at
terminals 2 and 4 in order to access the system. Such component may
be, for example, a browser plug-in or a stand-alone software
application.
Server 8 is preferably maintained by an independent party, who is
responsible for hosting all of the application software and
maintaining all of the databases associated with the preferred
embodiment. As will be explained below, certain data may be kept
secret from enterprise-users or investor-users, and thus server 8
is preferably maintained by a disinterested independent party whose
compensation is not directly derived from the funding or valuation
of any investments resulting from use of the system. This system
would thus provide no incentive for the independent party
maintaining server 8 to provide any advantage to another user of
the system by revealing any of the confidential information
maintained on the system.
With reference now to FIG. 2, the functionality of the software of
the preferred embodiment of the present invention may be described
in overview. Enterprise-user input block 10 represents various
forms of characterizing information related to the enterprise. Such
information would be entered through an enterprise-user terminal 2
as depicted in FIG. 1. That information may be of a general nature,
or in the preferred embodiment may be more detailed information
that is processed at enterprise characterization block 12, as
described more fully below. It should be noted that, except for
those instances when the information inherent to a unique input or
output block is explicitly described, all input and output blocks
shown in the figures described herein are intended to represent and
contain the information provided or received, respectively, by
users that is described for relevant attached blocks in related
figures.
In the preferred embodiment, information received at block 12 is
elicited through user prompts generated through the system from
server 8 by a graphical user interface appearing at enterprise-user
terminal 2. Subsequent prompts are preferably customized based upon
the characterizing information already entered by the
enterprise-user. A preferred set of prompts and allowable responses
is provided in the following table. The first column of this table
provides a unique query ID associated with each query, where an ID
with a non-zero value in the tenths position is a sub-query under
the matching ID with the same digit in the ones position and a zero
in the tenths position; for example, A2.1 is a sub-query under
query A2.0. The second column identifies any dependencies
associated with the query, that is, whether a particular answer to
another query is required in order for the query to be presented.
For example, for A2.0 the entry "A1.0=Y" means that query A2.0 is
only asked if the answer to query A1.0 is "yes." The third column
identifies the nature of the query. The fourth column identifies
the preferred type of input expected and allowed. The following
designations and abbreviations are used in the fourth column:
TABLE-US-00001 INPUT CATEGORY A: PRODUCT/SERVICE CHARACTERIZATION
A1.0 Does enterprise currently have a commercially Y/N available
product(s) or service(s)? A2.0 A1.0 = Y Provide a general
characterization of each commercially available product and
service: A2.1 Name Text A2.2 Description of key performance
attributes Text A2.3 Advantages relative to average competing
products Text A2.4 Disadvantages relative to average competing
Text; MC products A2.5 Number of current customer implementations
SNV A2.6 Current market share SNV A2.7 Barriers to competitive
emulation Text; MC A2.8 Does production utilize existing production
Y/N technologies? A3.0 A1.0 = Y Provide a technical
characterization of each commercially available product and
service: A3.1 Name MC A3.2 Is supporting technical data available?
Y/N A3.3 A3.2 = Y Data type MC A3.4 A3.2 = Y Data source MC A3.5
Have technical aspects been documented? Y/N A4.0 Does enterprise
currently have a product(s) or Y/N service(s) in development? A5.0
A4.0 = Y Characterize each product and service currently in
development: A5.1 Name Text A5.2 Key performance attributes Text
A5.3 Performance advantages Text; MC A5.4 Performance disadvantages
Text; MC A5.5 Projected time to fully functional prototype? SNV
A5.6 Projected time to fully functional commercial SNV product?
A5.7 Will the production of this product utilize existing Y/N
production technologies (commercially proven and available)? A6.0
Provide a technical characterization of each product currently in
development: A6.1 Are functional specs available? Y/N A6.2 List
possible barriers to advancement Text INPUT CATEGORY B:
INTELLECTUAL PROPERTY (IP) B1.0 Does enterprise currently have any
applied for or Y/N granted IP relating to product(s)? B2.0 B1.0 = Y
Choose which of the following IP has been applied for or granted
and the number of grants per type: B2.1 US patents - applied SNV
B2.2 US patents - granted SNV B2.3 International patents - applied
SNV B2.4 International patents - granted SNV B2.5 Trademarks -
applied SNV B2.6 Trademarks - granted SNV B2.7 Copyrights - applied
SNV B2.8 Copyrights - granted SNV B3.0 B2.1 > 0 Characterize
each applied and granted patent in B2.2 > 0 terms of the
following attributes: B2.3 > 0 B2.4 > 0 B3.1 US status MC
B3.2 B3.1 US life remaining SNV B3.3 International status MC B3.4
B3.3 International life remaining SNV B3.5 Inventor(s) Text B3.6
Product relation MC B3.7 Summary of abstract Text B4.0 Do any
products rely on any ancillary intellectual Y/N property? B5.0 B4.0
= Y Provide the following information for each product that relies
on any ancillary intellectual property: B5.1 Product dependent on
any ancillary IP? Y/N B5.2 Has contractual agreement(s) been
established with Y/N all ancillary IP owner(s)? B5.3 Average term
of exclusive right(s) to use SNV B5.4 Average term of non-exclusive
right(s) to use SNV B5.5 How many ancillary patents will require
usage rights SNV in order to commercialize product? B5.6 How many
ancillary copyrights will require usage SNV rights in order to
commercialize product? B6.0 How do you intend to protect your IP in
the future? MC INPUT CATEGORY C: BUSINESS CHARACTERIZATION C1.0
Provide a brief description (less than 100 words) of Text the
enterprise (this summary will be listed w/ company name in
investor-user search results) C2.0 Provide a comprehensive list of
keywords that Text identify the nature of your
product(s)/service(s), enterprise, and market(s). (These keywords
will enable investor-users to identify your enterprise when
searching via specific interests.) C3.0 How many years has
enterprise been in existence SNV (to the tenths)? C4.0 Characterize
the current status of enterprise's historic documentation and
formal planning: C4.1 Historic financial records - complete Radio
C4.2 Historic financial records - incomplete Radio C4.3 Historic
financial records - not attempted Radio C4.4 Historic financial
records - available for review? Y/N C4.5 Business plan - complete
Radio C4.6 Business plan - incomplete Radio C4.7 Business plan -
not attempted Radio C4.8 Business plan - available for review? Y/N
C4.9 Financial projections - complete Radio C4.10 Financial
projections - incomplete Radio C4.11 Financial projections - not
attempted Radio C4.12 Financial projections - available for review?
Y/N C4.13 Market assessment - complete Radio C4.14 Market
assessment - incomplete Radio C4.15 Market assessment - not
attempted Radio C4.16 Market assessment - available for review? Y/N
C4.17 Competitive assessment - complete Radio C4.18 Competitive
assessment - incomplete Radio C4.19 Competitive assessment - not
attempted Radio C4.20 Competitive assessment - available for
review? Y/N C5.0 Describe the enterprise's business model in the
following terms: C5.1 Structural model Text C5.2 Revenue model Text
C6.0 Describe how/why the company's specific business Text model is
optimal for the nature of its product, target market(s), and
competition: C7.0 Describe the company's market penetration and
competitive protection strategies: C7.1 Market penetration strategy
Text C7.2 Competitive protection strategy Text C8.0 Does the
enterprise have an established distribution Y/N strategy? C9.0 C8.0
= Y How will the majority of products/services be distributed in
terms of channel and delivery type: C9.1 Channel type MC C9.2
Delivery type MC C10.0 Does the enterprise have any established
supply- Y/N chain relationships w/ outside entities (supplier or
buyer)? C11.0 C10.0 = Y Quantify all established supply-chain
relationships in terms of the following criteria: C11.1 Number of
contracted supplier relationships SNV C11.2 Number of un-contracted
supplier relationships SNV C11.3 Number of contracted buyer
relationships SNV C11.4 Number of un-contracted buyer relationships
SNV C12.0 C10.0 = Y Describe each supply-chain partnership: C12.1
Name of distribution partner Text C12.2 Nature of partnership MC
C13.0 Does the enterprise have any established strategic Y/N
relationships (excluding distribution)? C14.0 C13.0 = Y Quantify
all established strategic relationships in terms of the following
criteria: C14.1 Number of contracted strategic relationships SNV
C14.2 Number of un-contracted strategic relationships SNV C15.0
C13.0 = Y Describe each strategic partnership: C15.1 Name of
strategic partner Text C15.2 Nature of partnership MC C16.0 A1.0 =
Y Provide the top 1-5 customers and their revenue contribution in
each historical fiscal year (FY): C16.1 Each customer name in each
historical FY Text C16.2 Portion of total revenue for each customer
in each SNV historical FY C17.0 Provide an employee headcount by
each function for each historical FY: C17.1 function MC C17.2 C17.1
Headcount by each function in each historical FY SNV C18.0 Provide
the projected employee headcount by function for each future FY:
C18.1 Function MC C18.2 C18.1 Headcount by function in each future
FY SNV C19.0 Has the enterprise employed the use of any Y/N
professional advisors? C19.1 C19.0 = Y How many hours of
professional advice have been SNV utilized? C20.0 Does the
enterprise have a functioning board of Y/N directors? C20.1 C20.0 =
Y How many board members? SNV C21.0 Is the enterprise or any of its
principals involved in Y/N any pending or threatening legal
action(s) or related proceeding(s)? C21.1 C21.0 = Y Describe the
threatening legal action(s) or related Text proceeding(s) C22.0
Does the company have any unsatisfied liens or Y/N judgments
against the company, any of its principals, or subsidiary(ies)?
C22.1 C22.0 = Y Describe the unsatisfied liens or judgments against
Text the company, any of its principals, or subsidiary(ies)? INPUT
CATEGORY D: MARKET CHARACTERIZATION D1.0 Provide a characterization
of each current target market segment for each company product and
service in terms of the following criteria: D1.1 Market name Text
D1.2 Description of target customer base Text D1.3 Target market
industry type MC D1.4 Current number of customers SNV D1.5 Current
market share SNV D1.6 Current total market size (in dollars) for
each market SNV D1.7 Projected 5-yr compounded annual growth rate
for SNV each market D1.8 Source of information for 5-yr projection
MC D1.9 Potential drivers of market growth and demand for MCC
product/service D1.10 Potential barriers to market adoption MCC
D1.11 Other potential general market risks Text D1.12 Describe the
basis for why you feel target market(s) Text will adopt your
various products, including the assumptions that underlie this
basis. D2.0 Provide a characterization of each long-term extensible
market segment of each product and service in terms of the
following criteria: D2.1 Market name Text D2.2 Description of
target customer base Text D2.3 Target market industry type MC D2.4
Time to market introduction MC D2.5 Current total market size (in
dollars) for each market SNV D2.6 Projected 5-yr compounded annual
growth rate for SNV each market D2.7 Source of information for 5-yr
projection MC D2.8 Potential drivers of market growth and demand
for MCC product/service D2.9 Potential barriers to market adoption
MCC D2.10 Other potential general market risks Text D2.11 Describe
the basis for why you feel target market(s) Text will adopt your
various products, including the assumptions that underlie this
basis D3.0 Describe the enterprise's general marketing strategy
Text INPUT CATEGORY E: COMPETITION E1.0 Characterize each
competitor in each market for which each company product and
service competes in terms of the following criteria: E1.1 Market
name MC (D1.1, D2.1 "Market Name") E1.2 Competitor name Text E1.3
Competitor product name Text E1.4 Competitor penetration maturity
MC E1.5 Competitor current market share SNV E1.6 Competitor
advantages relative to enterprise product Text; MC E1.7 Competitor
disadvantages relative to enterprise Text; MC product E2.0 Describe
the enterprise's strategy for maintaining or Text improving its
competitive position INPUT CATEGORY F: FINANCIAL F1.0 Indicate how
revenue and development costs are recognized: F1.1 Revenue MC F1.2
Development expenses MC F2.0 Where available, provide the following
annual financial metrics for each historical fiscal year (FY): F2.1
Each product number of units sold in each historical SNV FY F2.2
Each service number of customers in each historical SNV FY F2.3
Each product revenue in each historical FY SNV F2.4 Each service
revenue in each historical FY SNV F2.5 Other revenue in each
historical FY SNV
F2.6 Total revenue in each historical FY SNV F2.7 Direct cost of
each product revenue in each historical SNV FY F2.8 Direct cost of
each service revenue in each historical SNV FY F2.9 Direct cost of
other revenue in each historical FY SNV F2.10 Sales and marketing
cost in each historical FY SNV F2.11 General and administrative
cost in each historical FY SNV F2.12 Research and development cost
in each historical SNV FY F2.13 Depreciation and amortization cost
in each historical SNV FY F2.14 Total cost of operations in each
historical FY SNV F2.15 Other income in each historical FY SNV
F2.16 Interest expense in each historical FY SNV F2.17 Income taxes
in each historical FY SNV F2.18 Cash flow from operations in each
historical FY SNV F3.0 Where available, provide the following
year-end historical financial metrics for each historical fiscal
year (FY): F3.1 Cash and short-term investments in each historical
SNV FY F3.2 Accounts receivable in each historical FY SNV F3.3
Other current assets in each historical FY SNV F3.4 Capital assets
in each historical FY SNV F3.5 Accumulated depreciation and
amortization in each SNV historical FY F3.6 Accounts payable in
each historical FY SNV F3.7 Short-term debt in each historical FY
SNV F3.8 Long-term debt in each historical FY SNV F3.9 Paid-in
capital in each historical FY SNV F4.0 Indicate how financial
revenue and operational cost projections were established: F4.1
Revenue MC F4.2 Operational costs MC F5.0 Where available, provide
the following pro-forma projected operational and financial metrics
for the current and each future fiscal year (FY): F5.1 Each product
number of units for the current and SNV each future FY F5.2 Each
service number of customers for the current SNV and each future FY
F5.3 Each product revenue for the current and each future SNV FY
F5.4 Each service revenue for the current and each future SNV FY
F5.5 Other revenue for the current and each future FY SNV F5.6
Total revenue for the current and each future FY SNV F5.7 Direct
cost of each product revenue for the current SNV and each future FY
F5.8 Direct cost of each service revenue for the current SNV and
each future FY F5.9 Direct cost of other revenue for the current
and each SNV future FY F5.10 Sales and marketing cost for the
current and each SNV future FY F5.11 General and administrative
cost for the current and SNV each future FY F5.12 Research and
development cost for the current and SNV each future FY F5.13
Depreciation and amortization cost for the current SNV and each
future FY F5.14 Total cost of operations for the current and each
SNV future FY F5.15 Other income for the current and each future FY
SNV F5.16 Interest expense for the current and each future FY SNV
F5.17 Income taxes for the current and each future FY SNV F5.18
Cash flow from operations for the current and each SNV future FY
F5.19 Capital expenditures for the current and each future SNV FY
F5.20 Net change in working capital for the current and SNV each
future FY F6.0 How will the enterprise perform accounting and
financial control functions in the future: F6.1 Accounting MCC F6.2
Financial control MCC F7.0 Provide the company's projected
long-term SNV sustainable growth rate post term of financial
projections INPUT CATEGORY G: TECHNICAL PERSONNEL G1.0 For each
important research and development (R & D) employee, provide
the following criteria: G1.1 Name Text G1.2 Position MC G1.3
Primary enterprise product or service in which MC (A2.1, person is
involved A5.1 "Name") G1.4 Years of experience related to product
or service in SNV which person is involved G1.5 Highest level of
education obtained MC G2.0 B2.1 > 0 How is each product inventor
currently associated B2.2 > 0 with the enterprise: B2.3 > 0
B2.4 > 0 G2.1 Inventor (B3.5, "Name") MC G3.0 Does the company
currently have a CTO or Y/N development manager in place? G4.0 G3.0
= Y What is the name of the company's CTO or Text development
manager? INPUT CATEGORY H: MANAGEMENT H1.0 Provide the compensation
of each executive and management employee for each historical
fiscal year (FY) in terms of the following criteria: H1.1 Cash
compensation for each employee in each SNV historical FY H1.2
Equity compensation for each employee in each SNV historical FY
H2.0 Provide the following characteristics for each executive and
management employee: H2.1 Position description of each employee MC
H2.2 Name of each employee Text H2.3 Highest level of education
level obtained MC H2.4 H2.3 Was undergraduate not completed due to
pursuit of Y/N entrepreneurial opportunity? H2.5 H2.3 Type of
undergraduate degree MC H2.6 H2.3 Type of graduate degree MC H2.7
Years of total executive experience SNV H2.8 Years of experience in
the last 15 years relevant to SNV target industry H2.9 Years of
experience in start-up environment SNV H2.10 Years of experience in
the last 15 years working in a SNV similar company H2.11 Years of
marketing experience SNV H3.0 Which, if any, currently unfilled
executive and management positions will require filling in the next
2 years: H3.1 Each position MC H3.2 Anticipated months from now
that each position will MC be filled H4.0 Have any management
personnel previously Y/N founded a company(s) H5.0 H4.0 = Y How
many company(s) have been founded collectively by all executive and
management personnel and how many of those still operate as
standalone or acquired entities: H5.1 Company(s) founded SNV H5.2
Company(s) still operating SNV INPUT CATEGORY I:
CAPITALIZATION/VALUATION I1.0 Where available, provide the
following anticipated financing principal obligations and receipts
for the current and each future fiscal year (FY) (include capital
receipt from current offering): I1.1 New debt borrowing for the
current and each future SNV FY I1.2 New equity issuance for the
current and each future SNV FY I1.3 Existing debt principal
repayment obligations for the SNV current and each future FY I2.0
Provide the following company capitalization characteristics of
each existing executive and management employee: I2.1 Total
contributed equity capital for each employee SNV I2.2 Total
contributed debt capital for each employee SNV I2.3 Current equity
ownership for each employee (fully SNV diluted, pre-investment)
I3.0 Provide the following details of the enterprise's current
investment capital needs: I3.1 Amount of capital needed SNV I3.2
Type of capital investment available MCC I4.0 Provide an
itemization of how investment funds will be utilized: I4.1 Each use
MC I4.2 Capital budget for each use SNV I5.0 Does the enterprise
currently have an estimated pre- Y/N money valuation? I6.0 Would
you like to use the valuation calculator to Y/N establish a
competitively priced pre-money valuation for the enterprise based
on a comparison to your peer group? I7.0 I5.0 = Y Provide the
enterprise's estimated pre-money SNV I6.0 = N valuation Y/N = "yes"
or "no" Text = any alphanumeric characters MC = multiple choice MCC
= multiple choice cumulative (i.e., more than one choice is
allowed) SNV = single numeric value Radio = radio selection
button
It may be noted that in the preferred embodiment the input question
order and categorical organization are strategically performed in
order to minimize the ability of the enterprise-user to perform
top-down analytical rationalization and reconciliation of answers,
i.e., "game" the system. In addition, the input question
solicitation within each query category is structured to
dynamically adapt to the maturity and information availability of
the enterprise through the use of production rules as described in,
but not limited to, the second column of the above referenced
table.
Other classes of information may be input in a preferred
embodiment, including electronic business plans, digital video and
images, such as images of management personnel, electronic
information regarding products or services, electronic information
regarding tangible assets, and additional general information that
may be used to characterize the enterprise. The enterprise
characterization block 12 may also provide to the enterprise-user
the ability to block access to view by investor-users, certain
classes of information for purposes of confidentiality. An
investor-user wishing to review such information will thus be
required to contact the enterprise through an internal
communications system in order to see such information. Access to
the information will then be made available through an internal
communications system if consent is granted. Finally, the preferred
embodiment includes the capability at block 12 for the
enterprise-user to save his or her work if unable to complete all
the queries at one session so that they may be completed later. It
further includes the functionality to allow the enterprise-user to
update or correct any previously entered information at a later
time.
For a majority of enterprise agents entering a capitalization or
liquidation phase, determining a fair and competitive valuation for
their respective enterprise is often one of the most difficult
aspects. During the enterprise characterization process of block
12, enterprise-users are offered an automated enterprise valuation
calculator that enables enterprise-users to compare and
competitively establish the offering valuation of their respective
enterprise based on a comparison to aggregate peer valuation. This
process is comprised of the following steps. In the first step, the
valuation calculator incorporates the risk-adjusted valuation for
the specific enterprise as computed by enterprise analyzer block 42
described below. In the second step, the valuation calculator
solicits from the enterprise-user a premium or discount relative to
the median or mean RA-IRR of the enterprise peer group that the
enterprise-user desires. For example, the enterprise-user can
dictate that the enterprise-specific offering valuation be adjusted
so that the corresponding enterprise-specific RA-IRR is 5% below
the peer median RA-IRR. In the third step, the valuation calculator
computes an enterprise-specific offering valuation that when
reconciled with the valuation from step one through the process
described below for block 76, results in an enterprise-specific
RA-IRR that compared to the peer median or mean RA-IRR, duplicates
the discount or premium value set in step two by the
enterprise-user.
Input is preferably provided by enterprise-users at enterprise
input block 12 and is captured via web-based template forms that
dynamically conform to the specific domain cross-section of each
enterprise (i.e., enterprise type and maturity). Template
conformity is achieved through an initial enterprise type
characterization that determines the specific relevant template
and, subsequently, through solicitations during the input process
for qualifying information that enable a conditional presentation
of enterprise-specific information solicitation. The input
solicited consists primarily of ten (or more) categories of
enterprise attributes (e.g., education level of management) that
serve as the independent enterprise parameters (IEPs) for the
system, and empirical information for the archival section of
enterprise data 18; a list for the preferred embodiment is
described above. Inherent design modularity through categorical
organization of input criteria preferably allows for ongoing
alteration of input criteria. Additional parameters that are
non-essential to the output of the system may also be solicited, a
feature that obscures the computational focus of the system (i.e.,
prevents gaming of the system and reverse-engineering) and provides
additional empirical information for the archival section of
enterprise data 18.
Enterprise data block 18 is a data storage area that is fed by
information entered by the enterprise-user at input block 12. In
the preferred embodiment, enterprise data does not necessarily
represent a single physical data storage area; instead, it is a
logical construct that may represent areas of multiple data storage
areas. More specifically, enterprise data block 18 is an
information content component of a database (archival database 43
in FIG. 3, as more fully described below) containing empirical and
longitudinal information consisting of original and post-funding
performance characteristics related to the enterprise. Enterprise
data block 18 is also an information content component of a
database (knowledge base 40 in FIG. 3, as more fully described
below) containing analyzed and statistical correlation information
related to the enterprise that serves as a proprietary base of
statistical information.
Enterprise characterization block 12 feeds information to analysis
block 22, which will be described more fully below with reference
to FIGS. 3-6 and 8. Based on a general enterprise characterization
that originates at enterprise-user input block 10 and that is
processed at enterprise characterization block 12 for analysis in
block 22, various outputs for the enterprise-user are delivered at
block 24. This general characterization by the enterprise-user
consists of the type, location, and funding stage of the enterprise
and also the degree of enterprise planning and information
availability. Without necessarily incorporating the systems
described in FIGS. 3-6 and 8, analysis block 22 produces
information at enterprise-user output block 24 that may preferably
consist of the following. This output qualifies for the
enterprise-user the degree of information adequacy for full
submission. It preferably provides the capability to inform
enterprise-users of the number of member investor-users who possess
an investment focus profile that matches within a predetermined
statistical significance the profile of that particular
enterprise-user's enterprise. It also provides the capability to
inform the enterprise-users of any planning or information
inadequacies related to the enterprise and which are necessary for
comprehensive enterprise characterization at block 12.
Another feature of enterprise output block 24 is that, once an
enterprise is available for investor-user view in the system, the
enterprise-user has the capability to automatically check the
response rate of investor-users to the enterprise investment
opportunity, including preferably the number and general
composition characteristics of investor-users who have demonstrated
interest in the enterprise through various levels of content
exploration. Such levels may include, for example, access to the
enterprise summary, access to the enterprise business plan, and
initiation of a communication with the enterprise-user. The
enterprise-user may also review feedback that may be anonymously
provided by investor-users through the system via the internal
communication system.
Another type of possible communication from an investor-user at
output block 24 is that, in the preferred embodiment, the
enterprise-user may receive notification of an investor-user
request for controlled release and disclosure of previously
obscured enterprise information. The notification may be made
anonymously, but may also include a non-identifying profile of the
investor-user. The profile may preferably include an integrity
ranking of the investor-user that aggregates and quantifies any
negative feedback on the specific investor-user from other
enterprise-users.
Turning now to investor-user input block 14 and investor-user
requirements block 16, potential investor-users preferably have the
capability to create and save for recurring use multiple differing
enterprise search query profiles, each of which produces a list of
enterprises that possess enterprise-related attributes inclusive of
the specific criteria constraints of the search query profile.
Various search capabilities are included in the preferred
embodiment. Investor-users have the capability to construct
specific enterprise search query profiles that can incorporate an
extensive list of customizable search criteria in the form of
enterprise attributes. To satisfy the varying degrees of search
scope desired by investor-users, the search input form that is a
part of investor-user requirements block 16 preferably requires a
minimum of three criteria (e.g., enterprise type, maturity, and
location) while also providing a comprehensive list of additional
criteria for advanced investor-users who wish to perform more
specific searches. The investor-user may create original search
query profiles that can be constrained by one or more
enterprise-related criteria, where each independent criteria
restriction may preferably be quantitatively or qualitatively
varied to form an inclusive range or single restrictive end point.
Such criteria include all of the enterprise-related attributes
input by the enterprise-user at block 12, as well as the enterprise
classification type and funding stage; the location of the
enterprise categorized by region, state, city, zip code, or
distance from a chosen reference point; and the risk-adjusted
internal rate of return (RA-IRR) and risk value (the computation of
these values is described below). In addition, investor-users have
the capability to select the metric by which matched enterprises
are sorted in investor-user output block 26, these metrics being
the enterprise RA-IRR and risk value. Investor-users have the
capability to save multiple specific profiles for recurring use
when performing real-time searches of the enterprise database.
Investor-users also have the capability to automate search queries
so that an automatic alert (e.g., by email through the internal
communication system) is communicated to the investor-user in near
real time when an enterprise-user submits enterprise information
that matches the particular investor-user's enterprise search
profile. The investor-user's enterprise search characteristics are
retained and stored in investor-user data block 20 for recurring
use by the investor-user and for internal statistical analysis.
The investor-user preferably has the capability to adjust any of
the enterprise search criteria, computational methods, and
enterprise data of enterprise characterization block 12 at
investor-user requirements block 16 through direct manipulation of
an enterprise search performance diagram, presented through a
graphical user interface appearing at investor-user terminal 4. At
block 16, investor-users have the capability to customize, within
controlled constraints and for recurring use, the scoring
parameters, computation methods, and data source (i.e., knowledge
base block 40) used at analysis block 22 and further at Enterprise
Analyzer block 42 in the computation of enterprise RA-IRR, risk
value, and enterprise fair-market valuation. Specific investor-user
customization capabilities include, but are not limited to, the
capability to adjust weighting parameters as used by the risk
model; the capability to select the valuation modeling method
employed by analysis block 22 for computation and aggregation of
perpetual enterprise risk-unadjusted free cash flows, including but
not limited to: linear perpetual growth, multi-stage non-linear
perpetual growth, multi-stage partial-linear perpetual growth, and
residual income method models. Preferably, the system has the
capability to augment the perpetual assumption and requirement of
the methods described above by combining said methods with a
terminal valuation (i.e., fair-market valuation at projected fiscal
year of enterprise liquidity event or debt maturation) modeling
method that employs comparable valuations of enterprise peers,
including but not limited to enterprise fair-market valuations from
public market sources. The investor-user preferably has the
capability to dictate the risk model method (risk model #1 or #2)
that is employed by analysis block 22 for risk adjustment; the
capability to adjust the default data correlations (i.e., knowledge
base block 40) used by analysis block 22; and the capability to
select from a list of available enterprise-related attributes, with
specific attributes to be displayed with each enterprise listed.
Additionally at block 16, investor-users may have the capability to
adjust any of the enterprise data obtained at block 12 and
contained in enterprises database block 44 and as a result, produce
a corresponding analysis output from enterprise analyzer block 42
that reflects these enterprise data adjustment(s). Such
functionality is intended to provide investor-users the capability
to perform enterprise data scenario analysis for any
enterprise.
It is anticipated that a significant number of sophisticated
investor-users will wish to augment the relation and significance
of the default empirical and longitudinal correlations that are
referenced by the system for scoring and which are contained in the
knowledge base. This customization function allows the scoring and
resulting sorting of enterprises to conform, within controlled
constraints, to the specific enterprise screening preferences of
the investor-user. In the preferred embodiment, investor-users have
the capability to perform this augmentation through direct
graphical manipulation of the default correlations and their
significance. Additionally, when performing an enterprise search
and featured at block 26, a feedback system provides investor-users
an intuitive and heuristic graphical summary of resulting
enterprise search and sort output in the form of enterprise output
composition characteristics relative to prior customization
iterations and similar enterprise peer characteristics. These
investor-user augmentations of default correlations are retained in
investor-user data 20 for recurring use by the investor-user and
provide a source of information that is used to establish
independent investor-user decision-making correlations, and to
assist in resolving multi-colinearity uncertainties inherent to
correlation development block 50.
Investor-user data block 20 preferably comprises an information
content component of archival database 43 containing empirical and
longitudinal information related to investor-user enterprise
screening characteristics, analysis customization characteristics,
and investment decision characteristics. Investor-user data block
20 also preferably comprises an information content component of
knowledge base 40 containing analyzed and statistical correlation
information that serves as a proprietary base of statistical
information that is referenced by multiple components of the
system. In addition to storing information originated at block 16
and 26, investor data block 20 also stores information originated
at block 101 and 111.
The output generated by analysis block 22 at investor-user output
block 26 for potential investor-users may preferably include a
searched and sorted listing of enterprises, with a limited summary
accompanying each specific enterprise in listing. It may also
include the capability to provide with each specific enterprise
summary a number of associated enterprise attributes that are
specifically selected from a list of available attributes by the
investor-user.
In the preferred embodiment, much of the information generated at
investor-user output block 26 is graphical in form. It may include
the capability of providing investor-users with a report of the
enterprise search results that characterizes the enterprise
composition statistics of the search and allows for heuristic
refinement of the search parameters through direct manipulation by
means of a graphical user interface, this information of which is
stored in investor data block 20. The graphical enterprise search
summary profile includes each enterprise-related criterion adjusted
from the default value in the search profile displayed on the
x-axis of a graphical summary. Each such criterion features a
corresponding horizontal or vertical graphical bar that quantifies
the portion of enterprises included or excluded from the group
inclusive of the chosen criterion.
Investor-user output block 26 further includes the capability for
the investor-user to select and automatically receive for each
unique enterprise a summarized analysis that includes interactive
functionality and quantitative and qualitative information that
characterizes the specific enterprise investment opportunity,
including automatic multi-factor comparisons of an enterprise of
interest to relevant peer enterprises of enterprise database block
44. Such information may preferably include a probabilistic
quantification of the enterprise RA-IRR through a probability
density profile chart that illustrates the computed RA-IRR as a
function of corresponding probability for each of the range of
possible RA-IRR values. Such information may also include an RA-IRR
probability density profile for the median or mean of relevant peer
enterprises; a probabilistic quantification of the enterprise risk
profile through a radar illustration for each of the risk
categories quantified by the method; and a categorized risk profile
for the median or mean of relevant peer enterprises. Such
information may also preferably include the fair-market valuation
of an enterprise and direct (market) comparisons of that enterprise
to relevant peer enterprises in terms of quantified metrics for
risk (i.e., risk value) and return (i.e., risk-unadjusted internal
rate of return) as further computationally described in reference
to block 42.
Investor-user output block 26 also preferably includes the
capability to provide, through a database function in each
enterprise summary, an anonymous quantification of the amount of
specific enterprise page views by all investor-users, including but
not limited to the number of investor-users viewing that specific
enterprise summary; the number of investor-users viewing that
specific enterprise business plan; and the number of investor-users
contacting the enterprise-user. It also includes the capability for
the investor-user to, when specific categories of enterprise
information are obscured from unauthorized view by investor-users,
request authorization from the enterprise-user for access to
enterprise information through an internal communication system.
More generally, it includes a communication capability that allows
the investor-user to contact the enterprise-user via the internal
communication system with or without disclosure of the
investor-user's identity.
Other features according to the preferred embodiment of
investor-user output block 26 are the capability for the
investor-user to tag an enterprise with a certain hierarchical rank
relative to other enterprises, and the capability for the
investor-user to remove any enterprise from inclusion in the list
that is generated for that investor-user in response to a search.
Further, the investor-user preferably has the capability to
indicate a note of interest in a specific enterprise for purposes
of a syndicated investment with other potentially interested
investor-users, which, once enacted, may be seen by other
investor-users who select the summarized analysis for the specific
enterprise. Finally, investor-user output block 26 preferably
includes the capability for an investor-user, in cooperation with
an enterprise-user, to automatically obscure a specific enterprise
from view by other investor-users in their search results if and
when the level of investment discussions between the investor-user
and enterprise-user warrant authorization by both parties of this
action. If investment discussions do not result in a mutually
satisfactory result, the obscured enterprise can be reopened for
viewing by the enterprise-user.
Monitoring block 28 of FIG. 2 provides the capability to monitor
the progress of enterprises over a period of time. Its function
will be described in more detail below with respect to FIGS. 10-14.
It receives input and generates output for enterprise-users at
block 114/116, and receives input and generates output for
investor-users at block 101/112 and 111/113. Monitoring block 28
uses data from enterprise data block 18 and investor data block 20,
and also provides data to these blocks for purposes as will be
described below.
Referring now to FIG. 3, the functionality of analysis block 22 of
FIG. 2 may be described in greater detail, along with a more
detailed description of certain of the components identified with
reference to FIG. 2. Knowledge base 40 comprises a set of default
probabilistic reference correlations. These correlations are
generated as a result of an ongoing statistical analysis of the
data contained in archival database 43. This proprietary base of
statistical information is referenced by multiple components of the
system of the preferred embodiment and functions as dynamic
reference knowledge for this system. The dynamic nature of this
information reference system enables and supports the architectural
modularity inherent to the system. Inherent modularity in the
computation architecture of the system facilitates independent
alteration of component functions and, as a result, inclusion of
evolving dynamic reference information contained in knowledge base
40. The sorting of enterprises is based on a scoring assessment
that consists of either the future enterprise RA-IRR or probability
of failure (risk value), of which are independently computed by
enterprise analyzer block 42 and risk-model block 68, respectively.
The function of enterprise analyzer block 42 and risk-model block
68 will be described in greater detail below.
Enterprise-characterizing information obtained through enterprise
characterization block 12 and associated system output from
enterprise analyzer block 42 are retained in enterprise database 44
for efficient extraction. The output from enterprise database 44,
including peer enterprises, may be presented in enterprise-user
output block 24 or investor-user output block 26, as described
earlier with reference to FIG. 2. Preferably, enterprise database
block 44 may also provide peer enterprise data to block 42 for the
purpose of computations as described below. As with previous data
storage areas described, enterprise database 44 represents a
logical construct associated with a particular type of information,
and may or may not be associated with a separate physical database
from other information, as described above with respect to archival
database 43 and knowledge base 40.
Enterprises characterized in enterprises database 44 are searched
for matches with the input parameters from investor-user search
requirements block 17 at search block 46. Those that possess
attributes that are outside the range of acceptable investor-user
search parameters are excluded from the output of match search
block 46, are not sorted at sort block 48, and do not appear in the
resulting investor-user output at block 26. If, however, a specific
enterprise is: 1) excluded from the output due to predetermined
minor statistical deviation(s) from the range of acceptable
investor-user search parameters and, 2) possesses a RA-IRR score
that is greater or a risk value that is less than a predetermined
portion of the enterprises inclusive in the range of acceptable
search parameters, then that specific enterprise is preferably
included as a "relaxed" match at investor-user output block 26.
Enterprises in enterprises database 44 that are inclusive to
investor-user search queries as determined at match search block 46
are sorted in descending order (i.e., ranked) according to their
specific enterprise RA-IRR score generated by enterprise analyzer
42 at sort matches block 48 or sorted in ascending order (i.e.,
ranked) according to their specific enterprise risk value score
generated by risk-model 68 at sort matches block 48. As a result of
the processes performed by enterprise analyzer 42, search block 46,
and sort block 48, investor-users are presented a matched and
ranked list of enterprises at investor-user output 26.
It may be noted that archival database 43 of the preferred
embodiment is a proprietary database containing enterprise-related
endogenous and exogenous, empirical and longitudinal information
that includes but is not limited to original enterprise attributes
(e.g. CEO experience, enterprise maturity, financial projections,
etc) and the associated performance characteristics of the
enterprise. Investor-user enterprise search and screening
characteristics, their investment decisions, and other forms of
exogenous information are also captured by archival database 43.
The information accumulated by the database originates from various
sources. One source is the original enterprise input captured at
enterprise input block 12. Another source is the enterprise
monitoring sub-system 28 of the preferred embodiment, as will be
described more fully below. Investor-user input from investor-user
input block 14 is stored in archival database 43 as well. In
addition, archival database 43 contains output from enterprise
analyzer block 42 and survey information from enterprise-users and
investor-users who have used the system. Finally, information from
various third-party external sources may be included.
The relationship between knowledge base 40 and archival database 43
is controlled by correlation development and feedback block 50.
Archival database 43 may be statistically analyzed to identify and
quantify all potential and useful forms of parametric correlation,
including but not limited to the correlations between original
enterprise attributes and their relation to resulting enterprise
performance, and the screening characteristics and investment
decisions of investor-users. These correlations are then stored as
statistical information at knowledge base 40, for reference and use
by multiple components of the system. Continuous data mining and
correlation analysis of archival database 43 at correlation
development and feedback block 50 provides for the discovery of new
correlations and dynamic quantitative adjustment of existing
correlations within knowledge base 40. This active feedback
mechanism enables the modular probabilistic prediction systems to
incorporate new statistical reference information and conform their
predictive capability to ever-changing systematic and unsystematic
conditions that affect the performance of enterprises and
investment decisions of investors.
Referring now to FIG. 4, the function of enterprise analyzer block
42 may be described in greater detail. Information arriving through
enterprise characterization block 12 is fed to validate user input
block 60. This step provides an automated and augmentable method
for the validation of specific independent enterprise parameters
(IEPs) inputted by the enterprise-user in order to minimize invalid
or inconsistent IEPs, and hence minimize invalid output by the
system. Depending on the specific IEPs to be validated, one or a
combination of the following reference comparison methods is used
to perform validation of the IEPs at validate user input block 60.
In one method, validation of specific IEPs is performed through
comparison to casually related endogenous reference IEPs from the
same enterprise by direct relation to single IEP references or a
combinatory relation to multiple IEP references. For example, many
enterprise input parameters that are inherent to income, balance
sheet, and cash flow statements are interrelated such that direct
or indirect mathematical comparison of these parameters can test
for validation. For other types of input, validation of specific
IEPs may be performed through comparison to causally related
exogenous information referenced from knowledge base 40. Examples
of such information may include third-party economic projections
and empirical enterprise peer information. As a hybrid method,
information may be validated through a combination of exogenous
information referenced from knowledge base 40 and causally related
endogenous reference IEPs.
Depending on the specific IEP to be validated, various validation
methods may be utilized. For IEPs that are mathematically related
and that inherently require mathematical precision (such as
accounting balances involving financial values), validation is
achieved by mathematically comparing two or more IEP values in a
predetermined relationship (e.g., equation) to identify
inconsistencies between values. To identify the specific IEPs that
are likely incorrect in these comparisons, multiple relationships
that incorporate similar IEPs may be employed to narrow down
options and point to the likely incorrect IEP. When this method
cannot accurately be applied due to inherent imprecision and
uncertainty in the comparison, the range of values relative to a
certain predetermined standard error or statistical significance
about the median or mean value of reference parameters may be
compared to IEPs in order to identify specific IEPs that are not
consistent with the relevant range of reference values and,
therefore, must be invalid.
It may be noted from the description of various validation
techniques above that some types of information may be inherently
validated in real time, that is, as they are input by the
enterprise-user at enterprise characterization block 12. If an
input is in fact determined to be invalid, the decision to
determine whether real-time feedback may occur is shown at decision
block 62. For these specific IEPs that may be validated in real
time, the enterprise-user is made aware of any invalidated IEPs
immediately and requested to appropriately adjust (i.e., reconcile)
the invalidated IEPs and re-enter correct IEPs. When invalidated
IEPs can be identified in this manner, enterprise-users are made
aware of the likely specific invalidated IEP in order to assist the
enterprise-user in adequately reconciling invalidated IEPs.
When invalid input cannot be reconciled in real time through
enterprise-user feedback, processing moves to reconcile user input
block 64. Reconciliation is performed on each specifically
invalidated IEP by one of various methods. One method is automatic
adjustment of invalidated IEPs to a value that achieves the
precision inherent to a relevant predetermined mathematical
relationship, such as those inherent to financial accounting
parameters. When neither real-time feedback nor automatic
adjustment can accurately be applied due to inherent imprecision
and uncertainty in the comparison, reconciliation of invalidated
IEPs occurs through automatic adjustment of these IEPs to the
nearest boundary of the range of values of a predetermined standard
error or statistical significance about the median or mean value of
relevant reconciliation parameters referenced from knowledge base
40.
At compute risk-unadjusted free cash flow block 66, the system may
utilize validated financial IEP projections from validate user
input block 60 and, if required, reconciled financial IEP
projections from reconcile user input block 64 to compute
risk-unadjusted free cash flows available to the enterprise (i.e.,
excluding principal and interest debt liabilities); risk-unadjusted
free cash flows available to enterprise equity holders (i.e.,
including principal and interest debt liabilities); and economic
residual income. As described above, investor-users have the
capability to dictate the valuation modeling method the system
utilizes for computation of enterprise free-cash flows or economic
residual income. Alternatively and as described above,
investor-users also have the capability to utilize a terminal
valuation modeling method that employs comparable valuations of
enterprise peers, including but not limited to enterprise
fair-market valuations from public market sources obtained from
knowledge base 40.
At the completion of processing at compute risk-unadjusted free
cash flows block 66, the output in the preferred embodiment flows
to both adjust via risk-model #1 block 68 and adjust via risk-model
#2 block 70. With reference to the risk model of block 68, that
model computes the distribution of probable specific enterprise
failure to adjust each projected annual risk-unadjusted free cash
or residual income parameter(s) and the terminal enterprise value
by the corresponding probability distribution or mean value of
success. This adjustment incorporates into these values the
probability of dichotomous enterprise success and failure, and
thereby incorporates the probability of failure as the statistical
uncertainty inherent to risk-unadjusted projected free cash flows.
By contrast, with reference to the risk model of block 70, that
model utilizes the distribution of probable specific enterprise
free cash flow or residual income deviation from projected
risk-unadjusted free cash flow or residual income to adjust each
projected annual and the perpetual (terminal) free cash flow or
residual income parameter(s) by the associated probability
distribution or mean value of free cash flows or residual income
deviation. This adjustment incorporates into risk-unadjusted
projected free cash flow or residual income and terminal enterprise
value the probability of actual free cash flow or residual income
deviation from risk-unadjusted projected free cash flow or residual
income, respectively, and thereby incorporates the probability of
actual free cash flow or residual income deviation as the
statistical uncertainty inherent to risk-unadjusted projected free
cash flow or residual income and terminal enterprise value.
At adjustment method optimization (AMO) block 72, a statistical
comparison of the predictive performance of the models of blocks 68
and 70 to actual longitudinal parameters from archival database 43
enables determination of the optimal default model to employ in the
system. Alternatively, investor-users have the ability to choose
the type of risk model to employ in their specific customization of
the system at investor-user input block 16, and hence they have the
ability to dictate the type of dependent statistical correlation
factor to utilize: dichotomous enterprise success and failure or
enterprise performance deviation from initial projections, as found
in knowledge base 40.
From the adjustment to risk-unadjusted projected free cash flows or
residual incomes and terminal enterprise value performed through
either method of blocks 68 and 70, the compute risk-adjusted free
cash flow block 74 serves to compute and generate a probabilistic
distribution or mean value of risk-adjusted free cash flow or
residual income for each projected fiscal year and the terminal
enterprise value. Then at compute RA-IRR block 76, the system
computes the specific discount rate that equates and reconciles all
probability-distributed or mean values of risk-adjusted free cash
flows or residual incomes and the terminal enterprise value with
the independent current enterprise valuation provided by the
enterprise-user at block 12. The resulting discount rate that
equates and reconciles these values is equivalent to an independent
estimate of the future enterprise RA-IRR that can be expected by
investor-users. Further, block 76 is capable of computing the
fair-market valuation of an enterprise through a peer discounting
of its risk-adjusted free cash flows or residual incomes and the
terminal enterprise value by the mean or median value of RA-IRR for
relevant peer enterprises maintained within enterprises database
block 44. Enterprise database 44 is a logical construct, and may or
may not correspond to a separate physical data storage area.
Referring now to FIG. 5, the risk model processing of blocks 68 and
70 of FIG. 4 may be described in more detail, including the
investor-user feedback mechanism incorporated into that processing.
For purposes of the explanation of FIG. 5, the models of blocks 68
and 70 preferably work in an identical manner. The risk models of
blocks 68 and 70 effectively combine various standard statistical
operations in a process that is capable of incorporating the
feedback of dynamic reference correlations from knowledge base 40.
The risk models provide a method for quantifying the probabilistic
systematic and unsystematic risk (i.e., uncertainty) inherent to
enterprise-specific expected free cash flow or economic residual
income, and hence provide an incorporation of the uncertainty
associated with the cash flow parameters that serve as the standard
basis for asset valuation. Enterprise risk is quantified in this
system through parametric comparison of specific enterprise-related
endogenous and exogenous attributes to corresponding
risk-correlated parameters of a relevant cross-section of
enterprise peers. This method uses empirically based parametric
risk correlations to quantify the level of risk representative of
each enterprise-related characteristic attribute.
The quantified risk values are statistically aggregated in a
probability distribution and mean value, herein called the risk
distribution and risk value. Computation of the risk distribution
and risk value incorporates probabilistic risk functions that are
effectively weighted according to the relative statistical
significance of the associated empirical reference risk
correlations. In defining the dependent variable to be
representative of enterprise risk and correlated with
enterprise-related endogenous and exogenous attributes in knowledge
base 40 and used in the risk modeling steps of blocks 68 and 70,
two primary parameters are most significant in relation to the
uncertainty in expected enterprise free cash flow or residual
income. The risk model of block 68 from FIG. 4 (risk model #1)
utilizes dichotomous enterprise success and failure as the
dependent parameter and proxy for uncertainty. The risk model of
block 70 (risk model #2) utilizes the empirical degree of actual
free cash flow or residual income deviation from expected free cash
flow or residual income (respectively) as the dependent parameter.
Regardless of the dependent parameter employed, the same
computational process described herein is utilized for correlating
the dependent parameter with enterprise-related endogenous and
exogenous attributes and for determining enterprise-specific
risk.
FIG. 6 graphically represents the method for identification and
establishment of correlations between enterprise-related attributes
and the dependent parameter, and conversion of those correlations
to probability density functions with associated mean values. The
graphs on the left side of the figure depict known data for
differing enterprise-related attributes for a sample data set. Such
graphs can be processed to yield distributions for the dependent
parameter, as shown to the right side of the figure. Different
values of an enterprise-related attribute lead to different
distribution means.
Although empirical data suggest that standard linear curve fitting
appropriately models the data in many cases (as shown in the graphs
on the left side of FIG. 6), non-linear curve fitting is
implemented by standard methods if necessary in a preferred
embodiment of the invention. Where non-linear relationships that
cannot be reduced to a simple linear model are found, more
sophisticated statistical algorithms and programs are known in the
art that can fit non-linear models as complex as are necessary. The
mathematical form of the model is identified such that the
appropriate statistics program can calculate the values of the
parameters that give the best fit to the data. For example, a
typical method is to minimize the sum of the squares of the
residuals. Nonlinear parameter estimation is intrinsically more
difficult than linear curve fitting, but if the data indicate such
non-linearity, appropriate algorithms are implemented to allow the
determination of needed relationships. This process is explained in
Gozalo, Pedro et al., "Local Nonlinear Least Squares: Using
Parametric Information in Nonparametric Regression," Journal of
Econometrics 99(1), pp. 43-106 (November 2000), and Kachigan, Sam
Kash, Multivariate Statistical Analysis: A Conceptual Introduction
(Radius Press 1991), both of which are hereby incorporated by
reference herein.
Although in FIG. 6 the distributions are shown as being
symmetrical, this condition is not a necessary requirement for the
correlation to be established and used in the calculation of a risk
distribution. In addition to correlations between
enterprise-related attributes and the dependent parameter,
differing attributes may be correlated with each other. For
example, the ability to obtain debt capital and the educational
level of the business owner (both significant factors related to
new business development and survival) are believed to be
interdependent. Any enterprise-related attribute that is highly
correlated with another attribute provides new information at a
lower weight than if that attribute were independent. Here again,
if the relationship is nonlinear, ordinary correlation values may
not fully describe the degree to which two enterprise-related
attributes may be related. Partially enabled by acquired data,
properly combining evidence in such cases may be done using one as
the prior in a Bayesian analysis, where the posterior is the
combined evidence provided by the probability density functions of
the non-independent parameters. This process is explained more
fully in Gelman, Andrew et al., Bayesian Data Analysis (Chapman
& Hall/CRC 2000), and Gomez-Deniz, E., "The Esscher Premium
Principal in Risk Theory: A Bayesian Sensitivity Study," Insurance
Mathematics and Economics 25, pp. 387-395 (1999), both of which are
hereby incorporated by reference herein.
Data to be used in the calculations of the risk distribution and
risk value must be organized on a common basis to minimize
complexity in those calculations. Because of the large range of
values associated with input data, sample size variations between
the business types, and other factors, normalization procedures are
preferably used to ensure consistency in subsequent calculations.
The issue of normalization arises again in consideration of methods
to ensure case-to-case direct comparability and consistent
interpretation of the risk distribution and risk value in the
interactive sensitivity analysis.
To provide the reference information required for the calculations
at blocks 68 and 70, data are maintained in knowledge base 40. This
dynamic database is subject to ongoing correlation development as
described above, with older data that have become less relevant to
the current economic/business climate being replaced by updated
information. This prevents obsolescence and provides for a
dynamically adapting enterprise analysis system based on growing
transaction volume (i.e., increased empirical and longitudinal
information) and other feedback mechanisms in the system as a
whole.
Referring again to FIG. 5, the system includes a component of block
16 at investor-user customization of knowledge base block 82. It
provides several means for interactive investor-user input, and at
this step the primary use of such input allows the investor-user to
customize the computation of risk parameters using heuristic
information. For example, an investor-user might recognize that the
cash-flow management plans of an enterprise, perhaps reflected in
an index of liquidity, are significantly more important as a
predictor of (early) failure than is its perceived technology-based
competitive advantage. If, on the basis of precision of the
reference data (the "fit") and the data sample size, one
enterprise-related attribute correlation is automatically weighted
more heavily than is another, the investor-user can customize the
calculation of risk distribution and risk value by adjusting the
appropriate weighting factor. This investor-user customization is
stored in knowledge base 40 with an association to the unique
investor-user that generated the customization for recurring use by
that investor-user.
Referring now to FIG. 7 that describes the functionality of
investor-user customization of knowledge base block 82,
investor-users have the capability to directly adjust the reference
correlations and probability density functions described above. The
adjustments are made in the preferred embodiment by directly
manipulating graphical images on the user interface presented to
the investor-user at investor-user terminal 4. Interactive
manipulation of probabilistic data represents one mechanism that
enables less mathematically sophisticated investor-users to access
certain statistical operations needed for sensitivity analysis.
Once performed by a specific investor-user, these adjustments are
stored in knowledge base 40 with an association to the unique
investor-user for recurring use. Algorithms are known to allow
these techniques to work with both discrete (Bernoulli, geometric,
Poisson, etc.) and continuous (uniform, normal, bivariate normal,
exponential, circular, etc.) distributions. The adjustments made to
reference information result in related changes in computational
output.
With reference now to FIG. 8, an explanation may be provided of the
algorithmic processing within risk model blocks 68 and 70. The
process for the determination of the risk distribution and risk
value may be described as a series of discrete operations: 1.
Select all statistically significant reference correlations and
probability distribution functions for the specific type of
enterprise being assessed. These are drawn from knowledge base 40.
2. Select for the specific enterprise being assessed the
enterprise-specific attributes that correspond to the
enterprise-related reference attributes identified in step 1. 3.
Use the values from step 2 and the reference information from step
1 to determine the probability distribution function for the
dependent parameter that is associated with each enterprise-related
attribute of step 2, yielding y(A), y(B), . . . y(n). 4. Determine
the dependencies among enterprise-related attributes of step 1 and
adjust the result of step 3 according to the process described
above with respect to correlation development and feedback block
50. 5. For each enterprise-related attribute x, select R(x) from
knowledge base 40, where R=f(r, n), and where r=correlation
coefficient and n=sample size. R provides a weighting according to
the risk predictive reliability of each enterprise-related
reference attribute. 6. Using the values from steps 3 and 5,
compute the risk distribution and risk value according to the
equation: .beta.=R.sub.Ay(A)+R.sub.By(B)+R.sub.Cy(C)+ . . .
R.sub.ny(n). This equation provides a basis for a parametric model
in which each parameter has an associated probability and
determines the probability distribution associated with risk. 7.
Where needed and appropriate as an alternative to steps 5 and 6,
use Bayesian combination of evidence with sources provided by y(A),
y(B), . . . , to obtain the combined distribution y(.beta.).
In various embodiments, the systematic methods and processes
described above for block 42 and blocks 68/70 are capable of
incorporating many differing parameters of an enterprise and
statistical reference information in the generation of what may be
defined as multi-factor enterprise scoring values for an enterprise
and its relevant peers. These multi-factor enterprise-scoring
values may consist of, but are not limited to, a risk-adjusted
internal rate of return and risk value for the enterprise and its
relevant peers as described above, but in alternative embodiments
may include other values with utility in ranking enterprises.
As illustrated in FIG. 9, the system includes at interactive
sensitivity analysis block 80, the capability for investor-users to
view the results of the customizations to knowledge base 40 as
described above. For each iterative change of the default knowledge
base 40 values by investor-users, the resulting impact on
enterprise risk quantification is illustrated for a chosen
enterprise peer group such that investor-users can heuristically
assess the effect of incremental knowledge base 40 customizations
and, therefore, ultimately refine and conform the output of the
overall system to specific investor-user preference(s). In FIG. 9,
the effect of investor-user changes to default reference risk
correlations in knowledge base 40 are shown for each enterprise
within a specified peer group. While FIG. 9 demonstrates a
preferred embodiment, compositional characterization of resulting
enterprise peer group effects of knowledge base 40 customizations
is not limited to the format shown in this figure.
Monitoring component 28 from FIG. 2 may now be described in more
detail. Monitoring component 28 provides automatic and interactive
techniques for investor-users to monitor the operational maturation
process of enterprises and effectively identify when assistant
intervention is necessary. Investor-users have the capability to
utilize monitoring component 28 in two ways: (1) an automated
monitoring of enterprise performance through automatic analysis and
notification (i.e., an alert) directed toward the investor-user as
described for automatic enterprise monitor block 110; and (2)
interactive monitoring and analysis of enterprises through
investor-user use of interactive analytical functions as described
for interactive enterprise monitor block 140. In either case,
access is provided to investor-users through investor-user terminal
4, but the software necessary for these functions preferably
remains resident on server 8.
Monitoring component 28 allows investor-users to customize and
construct a specific progress monitoring profile for each unique
enterprise within their investment portfolio. Once a monitoring
profile is established, investor-users can request the
enterprise-users of their portfolio enterprises to periodically
access a web-based input system that is unique to their enterprise
and submit required enterprise progress information.
Enterprise-users may access the monitoring system through
enterprise-user terminal 2 for this purpose. Automatic use of the
system provides investor-users the capability to statistically
predict the future performance of the enterprise and the capability
to set benchmark deviation and threshold limits for each monitoring
parameter that function as triggering events and that generate
automatic investor notification if triggered. Interactive use
allows investor-users to perform in-depth enterprise performance
analysis through use of robust charting and analysis functions that
allow detailed analysis of monitoring parameters and the
information provided in the automated system.
Monitoring component 28 preferably provides the following
capabilities: (1) the capability to compare, for congruence, the
business development progress of an enterprise with enterprise
business plan projections; (2) the capability to determine and
moderate causes of sub-optimal enterprise performance; (3) the
capability to identify emerging risk factors and predict the
probable future performance of an enterprise; and (4) the
capability to provide early identification of incipient enterprise
failure in order to maximize the opportunity for proactive
preventative measures.
Referring now to FIG. 10, the specific components of a monitoring
system according to a preferred embodiment of the present invention
may be described. Investor-user inputs block 101 is used to feed
data to investor-user monitoring requirements block 100. When
constructing a monitoring profile, investor-users are offered a
wide range of enterprise monitoring metrics from which to choose.
While investor-users have the capability to choose and monitor
qualitative enterprise parameters, most monitoring metrics consist
of quantifiable parameters that in aggregate contain sufficient
information to adequately indicate to investor-users when
additional investigation of enterprise progress is warranted. For
each enterprise, the most informative and effective parameters of
which to monitor depend on the type and maturity of the enterprise.
While a universal core set of monitoring parameters may be utilized
for a majority of enterprises, each enterprise cross-section based
on enterprise type and maturity typically requires additional
monitoring parameters specific to the unique maturation and risk
factors of that enterprise cross-section.
Investor-users preferably have the capability to define and create
enterprise monitoring profiles through one or a combination of a
number of methods. In one approach, investor-users have the
capability to choose a default template of enterprise monitoring
parameters that contain a pre-existing set of parameters based on
the specific type and maturity of enterprise. Investor-users also
preferably have the capability to choose individual parameters from
a list of possible monitoring parameters, the list consisting of
those parameters contained in a default template. Additionally,
investor-users may have the capability to choose individual
parameters from a list of all possible monitoring parameters.
Monitoring parameters may include independent enterprise parameters
(IEP) obtained from enterprise-users, dependent values computed
from IEPs (e.g., financial ratios), or values obtained from sources
other than the reporting enterprise, such as independent service
providers of economic and business intelligence or relevant
subject-matter experts. Some monitoring parameters may include
enterprise financial metrics that require considerable accounting
resources on the part of enterprises and, therefore, limit the
reasonable reporting frequency required of enterprise-users by
investor-users (e.g., bi-monthly vs. quarterly). Parameters by
which enterprises may be monitored may include, but are not limited
to, those contained in the following table, as well as those shown
in the table presented above with respect to enterprise
characterization block 12. The first column of the following table
provides a monitoring parameter, and the second column identifies
either the input source of that monitoring parameter or the input
source of the information used to calculate that monitoring
parameter. With respect to the second column, the following
abbreviations are used:
TABLE-US-00002 CATEGORY A: FINANCIAL (historical) Total revenue
Enterprise- Component revenue for each product & service User
Growth rate in total revenue Growth rate in component revenue for
each product & service Gross profit margin for each product
& service Operating profit margin Net profit margin S&M,
G&A, R&D (nominal value and as a % of revenue) Direct cash
flow from operating, investing, and financing activities Capital
expenditures Free cash flow or cash burn Inventory turnover
Receivables turnover Payables turnover Working capital turnover
Fixed asset turnover Total asset turnover Cash cycle turnover
Operating cash turnover Quick ratio Cash flow from operations ratio
Defensive interval Total cash liquidity Days to capital depletion
(at time-weighted quarterly cash burn rate) Debt to total capital
Debt to total assets Fixed charge coverage ratio Return on assets,
total capital, and equity CATEGORY B: FINANCIAL (projected) Total
revenue Enterprise- Component revenue for each product &
service User Gross profit margin for each product & service
Cost of operations Free cash flow or cash burn Capital expenditures
Change in working capital Backlog/sales pipeline CATEGORY C:
BUSINESS DEVELOPMENT General development status Enterprise- Status
of distribution network, supply chain, strategic partners, and User
government contracts Number of customers or clients Customer or
client revenue concentration Number of employees (by function &
department) Revenue per employee Potentially valuable new
services/products Status of regulatory compliance Changes to
business or legal structure (restructuring, merger, acquisition,
joint venture, etc) Changes to ownership structure (excluding
internal equity compensation) CATEGORY D: PRODUCT/SERVICE
DEVELOPMENT IP status (patents, trademarks, copyrights) Enterprise-
Product performance status User Product unit cost status SME Timing
of product/service release ISP Capital efficiency of development
Technical barriers and limitations CATEGORY E: MARKET DEVELOPMENT
Current estimate of market size, growth rate, and total penetration
Enterprise- rate for each market of each product and service User
Current market share for each market of each product and service
ISP Status of barriers to market development Status of drivers of
market development Status of any evolving standards Potential new
market opportunities CATEGORY F: MANAGEMENT Management
effectiveness Enterprise- Management turnover User Relative
management compensation Projected employee headcount (by function)
CATEGORY G: COMPETITION Development status of competing products
and services Enterprise- IP status of competition User Change in
relative aggregate capitalization of competition SME Status of
emerging, potentially disruptive technologies ISP CATEGORY H:
EXOGENOUS ENVIRONMENT Leading economic index ISP Interest rates
Internal Equity capital availability CATEGORY I: OTHER Probability
of failure (computed by monitoring system) Enterprise- Specific
risk factors identified by enterprise analyzer or during due
diligence User Internal Change in accounting method(s) Notable
legal changes or events ISP = independent service providers SME =
subject-matter expert
Certain of the monitoring parameters identified above are explained
in more detail below: Category A: Financial (Historical) 1. Days to
Capital Depletion=(365/4)*(Cash+Marketable Securities/Time-Weighted
Cash Burn Rate for four most-recent quarters) Category C: Business
Development 1. General Development Status--The status of business
development compared to projected milestones in the original
business plan. 2. Status of Distribution Network, Supply-Chain,
Strategic Partners, and Government Contracts--Status of efforts to
establish or expand distribution network, advancement and
management of enterprise supply chain, number and context of
strategic partners, and status of current and future potential
government research and development cost-sharing contracts. 3.
Customer Revenue Concentration--Portion of revenue from each of the
top five customers or clients. Category D: Product/Service
Development 1. IP Status--Status of applied and granted, US and
international patents, and status of trademarks and copyrights. 2.
Product Performance Status--For each product, comparison of current
performance level(s) to those projected in the original business
plan. 3. Product Unit Cost Status--For each product, comparison of
current unit production cost to the original business plan unit
production cost curve for an evaluation of relative progress
towards cost-effecting technology advancements, production process
improvements, and volume production efficiencies. 4. Timing of
Product/Service Release--Comparison of current timeline for
product/service release to that of original business plan and prior
monitoring update. 5. Capital Efficiency of Development--A
long-term measure of the efficiency and effectiveness by which
capital is employed for technology development, which is quantified
in terms of monetary value. 6. Technical Barriers and
Limitations--Disclosure of any technical barriers to further
product advancement and any limitations of product applicability.
Category E: Market Development 1. Status of Barriers to Market
Development--Status of barriers to market development identified
during screening or due diligence process. Could include necessary
advancements in enabling ancillary technologies and limitations in
market adoption rate. 2. Status of Drivers of Market
Development--Status of drivers of market development identified
during screening or due diligence process. Developments or events
that may stimulate market demand, such as government legislation,
regulatory changes, and advances in enabling ancillary
technologies. 3. Status of Any Evolving Standards--Status of any
technological and regulatory standards that are evolving in a
relevant industry, especially with regard to competing standards
and the likelihood of market prevalence and resulting barrier to
the enterprise standard. 4. Potential New Market
Opportunities--Notable new markets for product(s) or service(s) of
the enterprise that were previously unidentified. Category F:
Management 1. Management Effectiveness--A quantitative metric that
characterizes the effectiveness of management in terms of capital
management and business, technology, and market development. 2.
Management Turnover--Any changes in key management personnel. 3.
Relative Management Compensation--A measure that quantifies
aggregate management compensation relative to various indications
of enterprise performance. This ratio is compared to the mean of
the peer group in order to assess the relative performance-based
compensation level of management. Category G: Competition 1.
Development Status of Competing Products/Services--Current
development status of competing products/services in terms of unit
cost and performance characteristics. 2. IP Status of
Competition--Intellectual property position of competing
enterprises and any potential resulting infringement by enterprise
or competing company. 3. Change in Relative Aggregate
Capitalization of Competition--Current and expected new
capitalization of competing enterprises that may engender excessive
product or service supply relative to projected target market size.
4. Status of Emerging, Potentially Disruptive
Technologies--Obtained via relevant subject-matter experts, the
current development, commercialization, or otherwise generation
maturation status of potentially market-disruptive, competing
technologies. Also, the probable timing of future milestones in
terms of performance, limitations, cost, and market introduction.
Category H: Exogenous Environment 1. Leading Economic
Index--Provided by the Economic Cycle Research Institute, a
quantitative, high-frequency leading index of U.S. economic growth.
2. Interest Rates--Measures of the cost of debt capital for various
durations. 3. Equity Capital Availability--A proprietary metric
that quantifies the relative availability of equity capital.
Category I: Other 1. Probability of Failure--(Enterprise
probability of failure will be described more fully below with
respect to predict failure block 124 of FIG. 11.) 2. Specific Risk
Identified by Enterprise Analyzer or During Due Diligence--Risk
factors specifically identified for the unique enterprise by the
system of the preferred embodiment or during the due diligence
process.
For each parameter of an enterprise monitoring profile,
investor-users have the capability to establish, and periodically
alter, reference limit values that are automatically compared to
IEP, dependent values computed from IEP, or values obtained from
sources other than the reporting enterprise. The reference limit
values serve as triggering events for automatic notification of
investor-users, as will be described in greater detail below. These
reference limit values include, but are not limited to, benchmark
deviation limits and thresholds limits. The benchmark deviation
limit may be defined as a certain value of standard deviation or
error about a reference benchmark value that may be selected such
that if the actual value exceeds the value of standard deviation or
error above or below the reference benchmark value, a triggering
event occurs. For example, if actual enterprise revenue exceeds a
predetermined degree of deviation from a pro-form a operational
revenue projection, a triggering event occurs. The threshold limit
may be defined as a certain single or multiple reference threshold
value(s) that may be selected such that if an actual value exceeds
a reference threshold upper or lower limit, a triggering event
occurs. Investor-users have the capability to utilize one of
several methods for the establishment of reference limit values
(i.e., triggering events) for each chosen monitoring parameter.
In establishing benchmark and threshold reference limit values for
comparison to actual future enterprise performance or exogenous
factors (e.g., interest rates), investor-users have the capability
to enter values at investor-user terminal 4 or employ information
contained in archival database 43 as the basis for benchmark and
threshold values, as will be described in more detail below. For
example, pro-form a financial projections originated by the
enterprise can be employed as the basis for reference benchmark and
threshold values by investor-users. Additionally, investor-users
have the capability to establish reference limit values through
selection of a relevant default template that contains standard
deviation or error and threshold reference values for each
monitoring parameter and that are based on the type and maturity of
the enterprise in question.
Investor-users have the capability to customize functional aspects
and output content of the monitoring system. The results of the
automated functions (described below for blocks 122, 124, and 126
in reference to FIG. 11) may be included or excluded in
investor-user output 112. If the function of predict performance
via risk-model 2 block 126 is selected for inclusion in
investor-user output 112, investor-users have the capability to
select, from an available list, enterprise operational metrics for
which the prediction of future performance will be computed and
featured in investor-user output 112. Investor-users also have the
capability to select, from an available list, any enterprise or
peer group related parameters that, as a result, are illustrated in
graphical form in investor-user output 112. For specific monitoring
parameters that exhibit preliminary indications of abnormal
deviation that are not confirmed by multiple data points (i.e.,
reporting periods), investor-users preferably have the capability
to place that parameter in a watch list that signifies the
parameter as requiring particular attention by the investor-user in
subsequent reporting periods.
Monitoring requirements database 102 is a database component that
is used to store the investor-user reference information and
functional customization settings described above with respect to
the discussion of monitoring requirements block 100. For each
enterprise monitoring parameter, this information may include, but
is not limited to: reference benchmark, reference benchmark
standard deviation or error limit(s), and reference upper and lower
threshold limit(s). Information sets (i.e., monitoring profiles)
contained in monitoring requirements database 102 are associated
with specific investor-users and respective enterprises and enable
the function described below with respect to monitoring parameters
for enterprise block 104 and characterize limit intersections block
120 of FIG. 11. Based on the unique logon identification of each
enterprise-user at block 106, the specific monitoring profile for
the associated enterprise is retrieved from monitoring requirements
database 102 at monitoring parameters for enterprise block 104.
For each defined monitoring parameter of a unique enterprise
monitoring profile that requires IEPs, corresponding IEPs are
solicited from associated enterprise-users through input
requirements for enterprise-user block 108. As described above,
some monitoring parameters are computed from and dependent on one
or more IEPs solicited from enterprise-users. The processing of
input requirements for enterprise-user block 108 thus solicits from
enterprise-user input block 114 the set of IEPs that provide the
information necessary to satisfy, or to compute from the IEPs, the
parameters of the monitoring profile from monitoring requirements
database 102. Automatic enterprise monitor block 110 is used to
perform various monitoring calculations, as will be described in
detail below. In order to enable the functions of automatic
enterprise monitor block 110, additional IEPs that are necessary
for those computations may also be solicited of enterprise-users at
input requirements for enterprise-users block 108.
As already explained, archival database 43 contains
enterprise-related endogenous and exogenous, empirical and
longitudinal information that includes but is not limited to
original enterprise attributes, time series enterprise performance
parameters, and exogenous parameters. This information may be
utilized by investor-users in establishing reference comparison
values at investor-user monitoring requirements block 100. To
populate archival database 43 with enterprise-related longitudinal
performance information, IEPs that are submitted through input
requirements for enterprise-user block 108 may be stored in
archival database 43. Information compiled or computed as part of
the functioning of automatic enterprise monitor block 110 may also
be stored in archival database 43 for reference and access by
various components of the system. A feedback mechanism allows
parametric enterprise reference information contained in archival
database 43 to be accessed by monitoring requirements database 102
for use in processing related to investor-user monitoring
requirements block 100.
Referring now to FIG. 11, the processing of automatic enterprise
monitor block 110 may be described in greater detail. Input
requirements for enterprise-user block 108 feeds the required
information (as described above) from the enterprise-user into
automatic enterprise monitor block 110. Automatic enterprise
monitor block 110 is comprised of characterize limit intersections
block 120, identify risk factors block 122, predict failure via
risk model #1 block 124, and predict performance via risk model #2
block 126.
For each monitoring parameter in which a reference limit value is
intersected by the actual value, characterize limit intersections
block 120 functions to identify the monitoring parameter and
determine certain other information. For intersections of benchmark
deviation or error limit values, characterize limit intersections
block 120 functions to find the associated limit value and degree
of deviation or error relative to the reference benchmark and limit
value. For intersections of threshold limit values, characterize
limit intersections block 120 functions to find the associated
limit value and degree of deviation beyond the limit value (i.e.,
the upper or lower limit value). Characterize limit intersections
block 120 also may obtain from archival database 43 all historical
limit value intersections involving monitoring parameters
including, but not limited to, the date of the intersection, and
the historical limit values (of whichever type is relevant), along
with the associated degree of deviation or error with respect to
each value.
Turning now to identify risk factors block 122, its function is to
utilize the operations as described with reference to FIGS. 6-8
above to compute the probability distribution and mean value of
future enterprise failure that is associated with each enterprise
attribute which is statistically correlated with peer enterprise
failure. In addition to utilization of the type of reference
correlations described above, it preferably incorporates potential
peer reference correlations between the independent events
described in reference to characterize limit intersections block
120 and dependent dichotomous enterprise failure, which are
contained in knowledge base 40. Utilizing the calculations
described in reference to FIGS. 6-8 and according to the product of
the mean risk value and associated weighting factor R(x), it then
ranks in descending order all enterprise-specific attributes (i.e.,
parameters) that are determined to be correlated with failure. It
illustrates with each ranked enterprise parameter the probability
distribution and mean value of future enterprise failure that is
associated with each enterprise parameter.
With reference now to predict failure via risk model #1 block 124,
its function is to compute the statistical probability of future
enterprise failure through utilization of the operations for risk
model #1 as described with reference to FIGS. 5-8 above. Risk model
#1 incorporates enterprise attributes as the independent variable
in reference correlations contained in knowledge base 40. In
addition to utilization of these types of reference correlations,
risk model #1 uses potential peer reference correlations between
the independent events assessed at characterize limit intersections
block 120 and dependent dichotomous enterprise failure, which is
also contained in knowledge base 40.
Predict performance via risk model #2 block 126 functions to
predict the risk-adjusted future value(s) of any actual enterprise
operational metric (e.g., revenue and free cash flows) by computing
the statistical probability of deviation of that value from the
corresponding risk-unadjusted projected value of the operational
metric through utilization of the operations for risk model #2 as
described with reference to FIGS. 5-8 above. Risk model #2
incorporates enterprise attributes as the independent variable in
reference correlations contained in knowledge base 40. In addition
to utilization of these types of reference correlations, it
incorporates potential peer reference correlations between the
independent events assessed at characterize limit intersections
block 120 and the dependent deviation as described above, which is
also contained in knowledge base 40. Alternatively, predict
performance via risk model #2 block 126 may predict the
risk-adjusted future value of any actual enterprise operational
metric through a non-linear adjustment of the extrapolation of that
metric from its current trend by a peer correlation that relates
the current (i.e., to-date) periodic trend in deviation to the
future periodic trend in deviation of actual operational value(s)
from projected operational value(s). In this process, it utilizes
correlations based on a peer group that associate the current
periodic trend in deviation of actual operational values from
projected operational values with the future periodic trend in
deviation of the actual operational values from projected
operational values. In addition to storing IEP information obtained
at input requirements for enterprise-user block 108, archival
database block 43 may store information generated at automatic
enterprise monitor block 110.
Output for investor-user block 112 provides for the delivery of an
output report that characterizes the maturation progress of the
enterprise and contains the information selected by investor-users
at investor-user monitoring requirements block 100. This report may
include, but is not limited to, a summary of the information
content generated by characterize limit intersections block 120 for
each reference limit intersection; a ranking of enterprise specific
risk factors (parameters), each of which features an associated
illustration of the probability distribution and mean value of
future enterprise failure that is associated with that factor; an
illustration that features both the mean value and probability
distribution of enterprise failure; for each operational metric
selected by the investor-user for prediction of performance as
calculated at predict performance via risk model #2 block 126, a
graphic illustration of the continuous trend in that metric for
both historical and future time periods; a graphic illustration of
primary pro-form a enterprise operational projections and the
deviation of actual values from those projections; a graphic
illustration of any enterprise or peer group related parameters
that are pre-selected for inclusion in investor-user output at
investor-user monitoring requirements block 100; the details of any
monitoring parameters contained in a parameter watch list; a
parameter history that features detailed information regarding
specific parameters, especially qualitative parameters with limited
or no potential for quantitative analysis; and the details of any
user notes previously created.
Output for enterprise-users block 116 provides for the delivery of
an output report that characterizes the maturation progress of the
enterprise. This report may include, but is not limited to, a
graphic illustration of primary pro-form a enterprise operational
projections and the deviation of actual values from those
projections; the identification of specific reference limit
intersections or near intersections (in order to create awareness
by enterprise-users of operational constraints and targets expected
by associated investor-users); and the identification and ranking
of enterprise-specific risk factors (parameters).
The monitoring system of a preferred embodiment of the present
invention comprises an interactive component, as illustrated in
FIG. 12. This system component is integrated with archival database
43, thereby providing investor-user access to all information
generated by block 108 and 110. This component enables
investor-users to interactively access and analyze information
contained in archival database 43 through interactive graphical
displays generated at interactive enterprise monitor block 140.
Examples of such displays are illustrated in FIGS. 13 and 14 as
inventory turnover ratio graphical display 148 and revenue
graphical display 155, respectively. Investor-users have the
capability to select, for graphic illustration and for a specified
term, a series of values for any parameter(s) within, but not
limited to, the following categories of parameters: historic and
projected operational metrics for a specific enterprise, including
prior operational projections that have changed; benchmark
deviation and threshold limit reference values established by the
investor-users at investor-user monitoring requirements block 100;
historic and projected parameters that are exogenous to the
enterprise (e.g., economic indicators); historic and projected
parameters computed at predict failure via risk model #1 block 124
or predict performance via risk model #2 block 126; and historic
and projected operational metrics for any or all enterprise peer
group(s) in the form of median or mean values. Investor-users have
the capability to graphically display any of the parameters
described in the form of a single parameter or multiple parameters
displayed simultaneously. The display can preferably be configured
to illustrate only values for a specified time period. For any
parameter in which an investor-user wants to signify as requiring
particular attention in subsequent reporting periods, the
investor-user preferably has the capability to place the displayed
parameter in a parameter watch list. In addition, if an
investor-user wishes to inquire concerning a specific enterprise
attribute, the investor-user may utilize the internal communication
system to send an inquiring communication to the relevant
enterprise-user.
For the parameter that is selected for display on the primary
dependent axis of the primary graphical display 148 or 155, a
history of the parameter's deviations for either benchmark or
threshold limits may be generated. The time period of this graphic
is dependent on and consistent with the time period selected for
the primary display graphic. Such a parameter display is shown by
the examples of parameter alert history 150 of FIG. 13 and
parameter performance history 154 of FIG. 14. For all parameters
that have intersected a benchmark or threshold limit, the history
of these intersections may preferably be featured in a scrollable
list that is organized in descending order according to the date of
intersection. For example, such a history is shown at parameter
alert history (all parameters) 152 of FIG. 13 and parameter alert
history (all parameters) 156 of FIG. 14.
The monitoring display as illustrated by the examples of FIGS. 13
and 14 may also include a multifunctional content reference
component, which is illustrated by multifunction sections 153 and
158 of those figures, respectively. The multifunction section of
each graphical display provides access to various forms of
enterprise monitoring information described as follows. The watch
list, when selected, features all monitoring parameters that have
been previously added by the investor-user to the parameter watch
list. For each parameter contained in the watch list, the following
information may be provided: the name of the parameter; the date on
which the parameter was added to the watch list; the value of the
parameter at the time that it was added to the watch list; the
value of the benchmark or threshold deviation or error at the time
of the parameter's addition to the watch list. The user notes
component, when selected, features previously entered user notes
and allows users to enter new content notes. Notes may be
categorized by association with a specific parameter or
characterized as general in context. Also preferably included in
the multifunction section, as illustrated by multifunction sections
153 and 158, is a risk factors feature which, when selected, lists
the enterprise risk factors as identified in identify risk factors
block 122 of FIG. 11. The risk factors are preferably ranked in
descending order with the associated probability distribution and
mean value of future enterprise failure illustrated with each
ranked enterprise parameter. Finally, the multifunction section may
contain a parameter history feature which, when selected, provides
detailed information regarding specific parameters, especially with
regard to qualitative parameters that have limited or no potential
for quantitative analysis.
Referring now to FIG. 15, enterprise analyzer block 42 and
associated blocks are presented according to an alternative
embodiment of the present invention. In this alternative
embodiment, enterprise analyzer block 42 and associated blocks
function together as an individual system and utilize the same
processes previously described for blocks 10, 12, 16, 40, 42, 44,
43, 50 and 82. With regard to block 160, it provides the capability
to inform enterprise-users of any planning or information
inadequacies related to the enterprise in order to qualify the
degree of information adequacy for comprehensive enterprise
characterization at block 12. Block 160 may also preferably provide
enterprise-users an analysis output result that includes all of the
quantitative and qualitative information computed by and previously
described for enterprise analyzer block 42, including the
fair-market valuation of an enterprise and direct data comparisons
of that enterprise to relevant peer enterprises in the form of
quantified metrics for risk (i.e., risk value) and return (i.e.,
risk-unadjusted internal rate of return). The direct data
comparisons described for block 160 between an enterprise of
interest and its relevant peers represents an aggregated market
presentation of risk and return for all enterprises contained in
block 44. In the alternative embodiment, much of the information
generated at investor-user output block 162 is graphical in form.
Investor-user output block 162 includes the capability for the
investor-user to receive for each unique enterprise a summarized
analysis that includes quantitative and qualitative information
that characterizes the specific enterprise investment opportunity.
Such information may preferably include a probabilistic
quantification of the enterprise RA-IRR through a probability
density profile chart that illustrates the computed RA-IRR as a
function of corresponding probability for each of the range of
possible RA-IRR values. Such information may also include an RA-IRR
probability density profile for the median or mean of relevant peer
enterprises; a probabilistic quantification of the enterprise risk
profile through a radar illustration for each of the risk
categories quantified by the method; and a categorized risk profile
for the median or mean of relevant peer enterprises. Block 162 may
also preferably include the fair-market valuation of an enterprise
and direct data comparisons of that enterprise to relevant peer
enterprises in the form of quantified metrics for risk (i.e., risk
value) and return (i.e., risk-unadjusted internal rate of return)
as computed by the processes described for block 42. The direct
data comparisons described for block 162 between an enterprise of
interest and its relevant peers preferably represents an aggregated
market presentation of risk and return for all enterprises
contained in block 44.
Referring now to FIG. 16, enterprise analyzer block 42 is further
presented according to an alternative embodiment of the present
invention. In this alternative embodiment, enterprise analyzer
block 42 and associated sub-system blocks function as previously
described.
Referring now to FIG. 17, risk model block 68/70 and associated
blocks are presented according to an alternative embodiment of the
present invention. In this alternative embodiment, risk model block
68/70 and associated blocks function together as an individual
system and utilize the same processes previously described for
blocks 10, 12, 40, 68, 70, 43, 50 and 82. As in the preferred
embodiment, the investor-user has the capability to select the
specific risk model (i.e., block 68 or 70) that is employed for the
computation of enterprise risk. With regard to block 164, it
provides the capability to inform enterprise-users of any planning
or information inadequacies related to the enterprise in order to
qualify the degree of information adequacy for comprehensive
enterprise characterization at block 12. In the alternative
embodiment, much of the information generated at investor-user
output block 166 is graphical in form. Investor-user output block
166 includes the capability for the investor-user to receive for
each unique enterprise a summarized analysis that includes
quantitative and qualitative information that characterizes the
risk of the specific enterprise. Such information may preferably
include a quantification of enterprise risk represented by a mean
value (i.e., risk value) and probability distribution (i.e., risk
distribution) of risk.
Such information may preferably include a probabilistic
quantification of the enterprise RA-IRR through a probability
density profile chart that illustrates the computed RA-IRR as a
function of corresponding probability for each of the range of
possible RA-IRR values. Such information may also include an RA-IRR
probability density profile for the median or mean of relevant peer
enterprises; a probabilistic quantification of the enterprise risk
profile through a radar illustration for each of the risk
categories quantified by the method; and a categorized risk profile
for the median or mean of relevant peer enterprises.
The present invention has been described with reference to certain
preferred and alternative embodiments that are intended to be
exemplary only and not limiting to the full scope of the present
invention as set forth in the appended claims.
* * * * *